The Forgotten Producer

The Effect of Higher Agricultural Commodity Prices on Producers in Sub Saharan Africa

ERASMUS UNIVERSITY ROTTERDAMErasmus School of EconomicsDepartment of Economics

Supervisor: Dr. E.O. Pelkmans-Balaoing

Name: Gerrit Hugo van HeuvelenExam number: 287480E-mail address: vanharro@hotmail.com

Table of Contents

1) Introduction______________________________________________________________4

2) Background_______________________________________________________________7

3) Overview of recent food price literature________________________________________8

4) The determinant of agricultural value added___________________________________14

5) Two different markets______________________________________________________17a) The Staple Market____________________________________________________________17

b) The Export Market____________________________________________________________23

6) The Cournot model________________________________________________________32a) Cournot model implications for a various number of competitors_____________________32

b) Horizontal mergers and the Merger Paradox______________________________________37

7) Price pass-through theory__________________________________________________39

8) Empirics________________________________________________________________41a) The Data_____________________________________________________________________41

b) The construction of the revenue indexes__________________________________________43

c) The Model___________________________________________________________________47

9) The regressions___________________________________________________________51a) The multiple regressions________________________________________________________51

b) The single regressions__________________________________________________________55

10) The results______________________________________________________________61

11) Conclusions_____________________________________________________________69

References:______________________________________Fout! Bladwijzer niet gedefinieerd.

Appendix:__________________________________________________________________75

2

Figures:

Figure 1: Official Bilateral Commitments to agriculture, forestry and fishing. (Real US $)___5Figure 2: Total ODA net disbursements to Africa (Real US $)__________________________5Figure 3: Cameroon’s Import and Export of Food and Animals_______________________13Figure 4: Kenya’s Import and Export of Food and Animals___________________________13Figure 5: Malawi’s Import and Export of Food and Animals__________________________13Figure 6: Rwanda’s Import and Export of Food and Animals_________________________14Figure 7: Ghana’s maize markets ($ per 100 kilograms, 1998)________________________18Figure 8: Cameroon (% change in price)_________________________________________20Figure 9: Kenya (% change in Price)____________________________________________20Figure 10: Malawi (% change in Price)__________________________________________21Figure 11: Cameroon (% of farmland used for the selected staple and export crops)_______23Figure 12: Kenya (% of farmland used for the selected staple and export crops)__________24Figure 13: Malawi (% of farmland used for the selected staple and export crops)_________24Figure 14: Rwanda (% of farmland used for the selected staple and export crops)_________24Figure 15: Revenue indexes of Kenya____________________________________________27Figure 16: Revenue indexes of Malawi___________________________________________27Figure 17: Revenue indexes of Rwanda___________________________________________28Figure 18: Cameroon: The retail price of maize in real local currency per kilogram.______29Figure 19: The amount of land used to grow export crops with fluctuating indexes________31Figure 20: Rwanda__________________________________________________________50Figure 21: Cameroon: Percentage change in VA and ST_____________________________57Figure 22: The tea price shock for Rwanda in 1991_________________________________58Figure 23: Kenya: Percentage change in VA and ST________________________________59Figure 24: Malawi: Percentage change in VA and ST_______________________________60Figure 25: Rwanda: Percentage change in VA and EX______________________________61Figure 26: Maize yield (tonnes per hectare)_______________________________________65Figure 27: Tea yield (tonnes per hectare)_________________________________________67Figure 28: Coffee yield (tonnes per hectare)_______________________________________68Figure 29: Tobacco yield (tonnes per hectare)_____________________________________68

Tables:

Table 1: correlations between producer and export prices____________________________26Table 2: The average revenue per hectare and the percentage increase in farmland from 1966 to 2005____________________________________________________________________31Table 3: The selection of agricultural commodities and their importance per country______45Table 4: LN regressions corrected for multicollinearity______________________________53Table 5: LN regressions corrected for multicollinearity______________________________53Table 6: The results ST_______________________________________________________55Table 7: The results EX_______________________________________________________56

3

“The test of progress is not whether we add more to the abundance of those who have

much; it is whether we provide enough for those who have too little”

US President Franklin D. Roosevelt

1) Introduction

The recent world food crises awoke the world. The importance of food, a commodity that

people in the West often take for granted, was stressed heavily. As people in rich countries

experienced faster rising food prices many people in poorer countries experienced once again

hunger. Although the potential negative effects of a food crisis are huge, a food crisis can also

have positive effects. One positive aspect of this crisis was the fact that it awoke the world to

see that something was awfully wrong. It displayed the backwardness of the agricultural

sector in some countries and caused men to notice this long neglected sector. Another positive

aspect was that some agricultural producers were now receiving a higher price for their

produce. For many farmers the food crises might actually be more of a blessing than a curse.

The real victims of the food crises however are the people that live on arid or semi arid land

or in urban slums in third world countries. Those are the poor in rural areas that cannot

produce enough food in order to survive and the poor living in urban areas. Both of these

groups of people are forced to buy food on the local market with the limited resources they

have. The rising food prices hit them the hardest. The eventual effect of higher agricultural

commodity prices, being good or bad, depends on how the world reacts.

The agricultural sector is for many countries, especially third world countries, an important

economic sector and the main source of employment. The livelihoods of most of the poor also

depend on this sector. Both economic theory and research have neglected this economic

sector and focussed more on the industrial sector and service sector seeing them as the

important sectors to develop. Bilateral and multilateral donors have also neglected this sector

as the share and level of official development assistance (ODA) going to agriculture has fallen

until recently (From around 8% in 1980 to less than 2% in 2005). 1

1 OECD data: credit reporting system , 2009

4

Figure 1: Official Bilateral Commitments to agriculture, forestry and fishing (Real US $)

0

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1967

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Figure 2: Total ODA net disbursements to Africa (Real US $)

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2006

Source: OECD

Due to the fact that the focus has been so heavily on the other sectors of the economy many

people have forgotten that the agricultural sector can also play an important role in the

development of a country. It is important to note that this sector most likely holds the key to

poverty reduction in third world countries. Agriculture does not have to be a backward or

inefficient sector; it can become a thriving sector as Brazil clearly demonstrates. Due to the

fact that two thirds of the world’s poor live in rural areas, most of them being dependent on

agriculture in some way in order to survive, we cannot afford to neglect it any longer (World

Bank 2007). Many people that climbed out of poverty did so by investing in education and

then moving to the cities. Many times staying in the rural areas meant choosing to stay in

poverty. Does this have to be the case?

5

The road out of rural poverty is something that is country specific and depends on many

factors. Improving land productivity and diversifying into commercial crops (mainly coffee)

was in Uganda the rural strategy that was most successful for escaping rural poverty. Market

liberalisation, exchange rate devaluation and favourable world prices led to an increase in

coffee prices which caused a substantial reduction in rural poverty from 1992 -2000. It is

estimated that in Uganda a 10% increase in the price of coffee reduces the poverty headcount

ratio by 6 percent (Deininger and Okidi 2003). In India the booming non-agricultural sector

indirectly caused growth and poverty reduction in rural areas. Basically the booming domestic

economy caused spill over effects into the rural areas. In Vietnam agricultural market

liberalisation caused many subsistence farmers to start growing more cash crops. This led to a

drastic fall in poverty rates as two thirds of the subsistence farmers became more market

orientated and saw their income almost double (World Bank, 2007).

An increase in agricultural income can be the cause of the expansion of the basic factors of

production, increase in productivity or through a rise in price. One proposal, that is heard

most often, is that higher prices for the agricultural sector’s produce will lead to higher

incomes and automatically therefore to lower poverty levels and more investments in that

sector. It is exactly this simple straightforward approach that would seem so easy to

implement in order to eradicate rural poverty and help the farmers. It is also exactly this

relationship between the price received for the produce and the income of the farmer that this

paper will investigate. The relationship between price and income is important to understand

and is not always as straightforward as implied by the simple strategy proposed above.

The main focus of this paper will be on Sub Saharan Africa (SSA) as the dependency on

agriculture and poverty rates are both highest in this region. For third world countries the

quantity of data is often limited and its quality sometimes questionable, especially when

referring to the agricultural sector and SSA. Due to this data restriction the paper will focus

mainly on four SSA countries Kenya, Cameroon, Malawi, and Rwanda. These countries in

SSA also differ significantly from each other in political, geographical, economical, climate

and other important conditions. Therefore the country or region specific context should not be

forgotten when reading this paper.

6

2) Background

Underplaying the importance of the agricultural sector is something that has not always been

the case in economics. In the beginning of the evolution of the economic thought some

economic thinkers stressed the importance of the agricultural sector. This of course was

mainly due to the fact that at this time, around 1700, agriculture was an important economic

sector in the then rich countries. The physiocrates viewed land and agriculture as the source of

all wealth. However when Adam Smith came into the picture the focus was shifting to

manufacturing production which was increasingly seen as the source of wealth. Using Adam

Smith’s concept of the division of labour the inefficiency of the subsistence/small farm

agricultural sector can clearly be demonstrated. The division of labour meant that a process

was broken down into many specific parts each person specializing in one part of the process.

This specialization would lead to efficiency gains. A farmer however has to be skilled in

many things, especially in third world countries. For example he has to be able to build his

house, plough, repair equipment and of course be a businessman. In the light of this concept

the inefficiency, which is often misinterpreted as backwardness, of the multi-talented

subsistence farmer is clearly demonstrated.

In the 1950’s and 1960’s the agricultural sector was viewed as a backward sector which was

of little importance for the real development of the country. The focus of development was

more on replicating the industrialization process in developing countries which led to an

urban bias. However the green revolution in Asia proved that the adoption of science based

technology could accelerate agricultural growth. This led to agricultural development coming

back on the agenda due to this new insight in the 1970’s and 1980’s. In the 1980’s many

African countries made policy reforms with respect to the agricultural sector. However the

results of the African green revolution were mixed and not as widespread as those

experienced in Asia as Africa’s agricultural productivity barely improved. That Africa had

failed to replicate the Asian green revolution became painfully clear which in the 1990’s led

to “agro-pessimism” especially when referring to SSA. Until quite recently the agricultural

sector has been out of the picture, the recent food crises changed that. What the results will be

of this renewed focus on the agricultural sector in SSA depends on how the world reacts.

A mistake that should not be made is that the Western capital intensive way of farming

becomes a short term model for SSA countries. The larger modern farms of the West are often

7

a lot more efficient. Third world countries, however, should not and cannot make this shift to

Western farming methods. The factor endowments of a country must be taken into

consideration when looking at its comparative advantage. Third world countries are heavily

endowed with low educated workers and capital is scarce. A quick shift to a Western way of

farming is neither probable nor desirable and would lead to massive unemployment and

therefore an underutilization of its resources. However there are many ways in which the

farmers in Africa could increase their produce and efficiency without radically altering the

whole agricultural system. This first of all requires massive investment into the agricultural

sector. Unfortunately many projects that have been set up by African governments and donors

have often been heavily under funded and plagued with corruption and therefore short term

projects. The state of infrastructure, human capital, irrigation, and access to credit has

improved in some countries but is still very meagre when looking at rural areas. These are

major constraints to the real development of this sector.

3) Overview of recent food price literature

Economic research has concentrated mostly on the effect of food prices on poor household in

third world countries; these are commodities that are consumed locally. Higher export prices

are expected to benefit a country, as export products are hardly consumed domestically, they

will therefore not be discussed in this section. The impact of higher food prices on poverty

depend a great deal on the distribution of net buyers and net sellers of food in a low income

country (Aksoy and Isik-Dikmelik 2007). Net buyers (sellers) are households that have

purchases (sales) of defined food products which are greater than the sales of similar products.

Poor household even in rural areas are usually net food buyers. The World Bank estimates

that the poor in developing countries spend roughly 60 percent of their income on food.2 It is

also estimated that poorest households spend roughly three quarters of their income on staple

food (Cranfield, Preckel and Hertel 2007). The general consensus has therefore been that

higher food prices will have a negative impact on the poor (Ravallion 1989, Byerlee, Myers

and Jayne 2006, Ivanic and Martin 2008). Although Ravallion argues that after 3-4 years the

price increases could, through secondary effects in the labour market, lead to higher incomes

for the poorest.

2 However it is important to note that the World Bank estimates that poor in SSA spend on average 53 percent of their income on food products which is lower that the developing world’s average.

8

The focus of most papers with regard to agriculture and poverty reduction has been on

agricultural productivity improvement. The extent to which prices react to productivity

changes depends on the tradability of the crop and the price elasticity of demand

(Christiaensen and Demery 2007). If markets are poorly integrated due to the high transaction

costs and the demand is price inelastic a productivity increase could cause a price collapse.

This would cause farmers to disinvest in the following year which will cause a rise in price

and a fall in productivity. Therefore if a commodity has an inelastic price demand and a low

tradability, like many staple crops in SSA, productivity gains are not sustainable. The

agricultural food market in SSA is also demand constrained. This means that the output level

is limited by the components of aggregate demand. Given the fact that most food markets in

SSA are relatively closed from net exports the output level will only be determined by the

domestic aggregate demand (consumption, private investments and government expenditure).

Higher output will therefore lead to lower prices and visa versa when the aggregate demand is

price inelastic.

The consensus has therefore been that a net food buyer will benefit from higher food

productivity due to lower prices. A fall in food prices will most likely lead to higher real

wages for the net food buyers. Higher productivity will only leave the net food seller better of

if the price decrease is smaller than the output increase. Net sellers will, most likely, not

benefit from an output increase given the fact that the demand of most staple crops is inelastic

and the fact that it operates in a relatively closed market. However, given the fact that most

poor are net food buyers, an increase in productivity will be pro poor.3

The pro poor myopic vision that most researchers have with regard to developing countries is

troublesome. While poverty focus is obviously important, it should not be the only focus. It

should not be forgotten that net sellers, even though they form a smaller group, are also often

poor in SSA. Therefore making agriculture more attractive, so that the necessary investments

can take place and farmers can start making a decent living, is also of importance and could

lead to long term benefits. When focussing only the short term poverty effects the potential

long term benefits are sometimes forgotten. Long term agricultural growth will have an

enormous impact on poverty reduction. Growth in the agricultural sector delivers more

poverty reduction in SSA than growth in any other sector (Dorosh and Haggblade 2003). It is

also estimated that for low income countries a 1 percentage point growth in the agricultural

3 However this is only in the short run.

9

sector yields 3.6 times more poverty reduction than growth coming from another sector in the

economy (Christiaensen and Demery 2007).4 Therefore the importance of stimulating

agricultural growth in SSA countries should not be underestimated especially with regard to

long term poverty reduction.

The effect of higher food prices on poorer households has been researched extensively.

However, only a few studies have focussed on the entire income distribution effect of higher

food prices. The income of one of the poorest groups in low income countries, farmers, may

increase due to high commodity prices (Hertel, Ivanic, Preckel and Cranfield 2004). A recent

study performed by Aksoy and Isik-Dikmelik has tried to take this overall effect into account

when looking at higher food prices. They focus on nine low-income countries and make an

important distinction between marginal food buyers and significant food buyers. Marginal

(significant) food buyers are food buyers whose net purchases of food constitute only a small

(significantly large) portion of their incomes. They claim that half of the net food buyers in

low income countries are marginal buyers thus a price increase will only have a small effect

on their income. This claim however might be the result of the selection of countries made by

the authors as only three SSA countries are included in the sample of which two, Zambia and

Madagascar, are by African standards relatively urbanized.5

The second conclusion that they draw is that the average income for net food sellers is lower

than the average income of net food buyers in eight of the nine selected countries. This, of

course, is partly because many of the very rich in low income countries are net food buyers

which raises the average. The authors also do not mention if they correct for cost of living

which is important as net food sellers usually live in rural areas where the cost of living is

lower than in urban areas. This, to a certain extent, will explain the difference between the

two income averages. The authors, however, conclude that higher food prices will lead to an

income transfer from richer (read: net food buyers) to poorer (read: net food sellers)

households and therefore be pro poor. Given the clear weaknesses of this study and its limited

scope the conclusions are relatively weak. However they do provide evidence that higher food

prices, even though they have a negative effect on the very poor, can have a positive effect on

4 However the same research points out that this effect of agricultural growth on poverty becomes weaker as the country becomes richer.5 The in 2007 estimated urban population(% of total) for Mozambique and Zambia was 36% and 35.3%.(source: WDI)

10

the incomes of the farmers. The distinction between marginal and significant food buyers

indicates that the negative impact of higher food prices is probably limited.

The recent food crises also gave insight as to how rising food prises effect the world. In a

recent report published by the World Bank, Global Economic Prospects 2009, an attempt was

made to estimate the effect that the rising food prices had on the poverty rates of low income

countries. This was done by using the Global Income Distribution Dynamics model of the

World Bank. This model gave insights as to which regions and populations groups are

vulnerable to higher food prices (See appendix 3 for a table with the results). They estimated

that the urban poverty headcount ratio in developing countries will rise faster than the rural

headcount ratio especially when referring to SSA (World Bank 2008). It is estimated that in

SSA the urban poverty headcount ratio will rise by 1.7 percent while the rural poverty

headcount rate is estimated to rise by only 0.3 percent due to the higher prices. Rural

household are likely to be hit less hard because that they also often produce the crops which

experienced a price rise. There is also evidence that correlates higher agricultural income with

higher non-farm activity income in rural areas (Haggblade, Hazel and Dorosh 2007). This

could also be a possible explanation as to why rural poor will be hit less hard than urban poor.

Agricultural income could possibly rise as a result of the higher food prices. Then, given the

positive correlation, the income of non-farm activity is also expected to rise in rural areas,

thus limiting the impact on the poor.

Overall it was estimated that SSA will not experience a massive rise in poverty in comparison

to some other regions due to the world food crises. According to the estimations East Asia &

Pacific and the Middle East & North Africa are the regions that will be hit the hardest by the

higher food prices. The World Bank stated that the recent food crises hit Africa less hard

because the price rose on average by less and large portions of the population live in rural

areas. In Africa the real food price rose on average by 8.3 percent while real food prices in the

Middle East rose by 19.8 percent (World Bank 2008). The lower food price rise in many low

income countries could be attributed to the fact that not all the food products consumed are

traded, especially when referring to SSA. Therefore the larger the share of none traded food

products in the diets of people within a country the lower the impact of the food crises. As

non traded food product prices are only determined by local supply and demand. Also during

2007 and the first half of 2008 the dollar, the currency that dominates international food trade,

11

was depreciating. This also caused that the local currency food prices to rise by less than the

dollar prices.

The policy changes of the mid 1980s and the early 1990s led to agricultural trade

liberalisation in many SSA countries. In theory globalization should lead to a more direct

price transmission from international markets to local producers (Dixon, Tanyeric-Abus, and

Wattenback, 2004). A case study of four SSA countries performed by Dixon, Tanyeric-Abus

and Wattenback (Smallholder responses to Globalization: African Field Experiences, 2004)

tried to measure the impact of policy reform on the farmers. They conclude that the impact of

trade liberalisation (Read: policy reform) is mixed in SSA. Market access and transport costs

are a key in determining the degree of price transmission. For many traditional export

commodities the price was largely transmitted to the farmers however for many food crops

the price transmission was a lot lower. However farmers producing traditional export

commodities were still faced with lower prices as the world prices of many of these export

commodities declined between 1990 and 2000. For example Cocoa declined by 29 percent,

Coffee by 9 percent and Cotton by 28 percent (WTO, 2001). The policy reforms have

unfortunately also led to an inflow of cheap imports from abroad which have had negative

effects on the local food and livestock markets. The higher transport costs of landlocked

countries have therefore worked two ways. It has depressed export potential of local

agricultural commodities however it has also provided natural protection to farmers from

cheap imports. In the cases of Malawi and Rwanda droughts and the Rwandan genocide have

heavily influenced the level of imports and exports. The genocide in Rwanda caused the value

of food and animal export to fall drastically while imports soared to levels higher than the

export value.6 Both these two countries are landlocked however domestic shocks have caused

these local markets to be flooded with imports from abroad. The level of imports of

agricultural commodities and livestock have increased drastically in these four countries (See

figures 3, 4, 5 and 6).

6 Food and Animals is a category of the FAO that contains all agricultural commodities and livestock.

12

Figure 3: Cameroon’s Import and Export of Food and Animals

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Figure 4: Kenya’s Import and Export of Food and Animals

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Figure 5: Malawi’s Import and Export of Food and Animals

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Figure 6: Rwanda’s Import and Export of Food and Animals

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Import value (1000$) Export value (1000$)

Source: FAO

The benefits of higher food prices for a farmer are probably less than that they at first appear

to be. This is partly due to the fact that price rises on international markets do not

automatically translate into domestic price rises. For example the internationally traded and

dollar dominated food products rose on average by 54 percent however the food prices in SSA

only rose by 8.3 percent (World Bank 2008). The benefits are also probably less than that they

at first appear to be because a rise in income does not only depend on what they produce but

also on the net sales of these goods. Food prices, especially staple food prices, can influence

the level of inflation. Both higher food and fuel prices resulted in a sharp rise in inflation

which could have become a real threat if financial crises did not come along. Therefore if

higher food prices lead to a rise in inflation the farmer’s initial gains will erode. One must

also not forget that higher agricultural commodity prices might be the result of higher input

prices or handling costs and could trigger imports. It would therefore be wrong to state that a

higher price for an agricultural commodity will automatically translate into a higher income

for farmers.

4) The determinant of agricultural value added

What variables affect the income of the farmers in SSA? One economic variable that is very

closely related to income is the variable value added. A farm’s value added is the difference

between its sales and the costs of raw materials and unfinished goods. Value added is the

source of income for the factors of production employed by the farm. Given the fact that most

farms in Africa mainly use two factors of production, land and labour, the value added is a

good reflection of the income of farmers and other agricultural workers. Therefore this

14

variable can be taken as a proxy for the income of the farmer. It is therefore of importance to

understand what factors influence the value added of the farmers in SSA

A farmer creates value added by transforming raw materials and unfinished goods into

commodities it can sell. Basic economic theory states that value added is the difference

between the revenue received and the cost made to produce the product. Generally speaking

the costs of an African farmer are his or her seeds, fertilizer (if lucky), pesticides, labour and

land. These factors are the major, if not all the, inputs into his production process. Irrigation is

not included in this list because many African farms are dependent on weather irrigation on

which they no influence. His revenue is the price he receives multiplied by the quantity of the

agricultural commodity produced on his land. It would therefore seem logical that the more

land, fertilizer, pesticide, labour and seeds are used the higher the potential output and total

value added will be. A higher price for the agricultural produce is also expected to lead to a

higher value added. However it must not be forgotten that weather patterns could disrupt these

direct relationships.

Value added per agricultural worker tells a different story than the total value added. The

value added per worker will only increase, given the same weather, if the difference between

the costs and sales increase. This basically implies that efficiency gains have been made by

the farmer. If the amount of farmland increases proportionate to the increase in agricultural

workers and all other factors remain constant the value added per worker will not increase

while the total value added will. Therefore the value added per agricultural worker is a better

proxy for advances in the income of the agricultural worker, as it to a certain extent measures

efficiency gains.

Joseph Owour (Determinants of agricultural productivity in Kenya, 1996) mentions the

amount of farmland, degree of commercialization, crop mix and intensity of fertilizer use as

determinants for regional differences in family labour productivity (value added) in Kenya.

Farmers growing commercial crops had a higher value added, as did farmers with more land

and with a crop mix. It is important to note that these three determinants are to a certain extent

linked to each other. As a farmer with more land has the opportunity to grow a crop mix and

therefore commercial crops. Even though value added of commercial crops was higher many

households in Kenya did not produce them because they put their food needs first. This,

according to the author, implied that rural consumers could not rely of the local food market

15

for their food needs. It was therefore of more importance for rural households to grow the

necessary food products than to grow commercial crops.

The level of agricultural investment, being public or private, should also influence the

agricultural value added. Case studies performed on India and China by the IFPRI

(International Food Policy Research Institute) showed that agricultural public spending is

most effective if it is allocated to agricultural research, education and rural infrastructure

(Shenggen 2008). If government agricultural spending is allocated effectively then it will

have a positive impact on agricultural growth and value added. However often governments

in Africa have not allocated government spending in such a way that it benefitted the rural

population. Even if government funds were allocated correctly the government spending often

only had a limited impact due to corruption (Shenggen 2008).

A country in which more investments have taken place could have a higher agricultural value

added. Investment in other sectors than the agricultural sector could possibly indirectly benefit

the agricultural sector through spill over effects, as was the case in India (World Bank 2007).

Therefore one could expect investments to have a positive effect on the value added of the

agricultural worker. However this, to a certain extent, depends on the type of investments

taking place because investments in natural resource exploitation will, most likely, not lead to

large spill over effects to the agricultural sectors. Bolivia for example is a country where

massive inflows of FDI have entered the hydrocarbon sector however these inflows have

barely affected the rest of the economy (World Bank 2005). Therefore the type of investment

will matter in determining its impact on the agricultural value added. Impact will also depend

on the balance between gross capital formation and gross capital depletion or destruction. If

the destruction of capital is greater than the formation of it then the country as a whole will be

worse of.

Another factor that has effect on the value added and therefore income of farmers is the

weather. The smallholders in Africa are often weather irrigated therefore shocks in the

weather will have huge implication on the quantity produced and therefore value added. It is

estimated that for developed countries 10 percent of the yield variability can be attributed to

weather changes (Gommes 1999). For developing countries where the irrigation is almost

solely dependent on weather the yield changes will be even more weather related.

16

5) Two different markets

In order to understand the relationship between price and income an important distinction

should be made between the final consumer price paid for the final produce (retail price) and

the producer price paid for the raw commodity (producer price). The retail price is the price

paid by the consumer for the final product at the end of the value chain, for example the price

of a cup of coffee at a restaurant. In the final product the coffee beans of the farmer are only

one part of the many additional inputs and processes the coffee beans have been through

before they reach the final consumer. The producer price is the price the producer receives for

its produce minus any vat or other deductible tax and transport cost. The value chain process,

through which different agricultural commodities go through before they reach the final

customer, differs per commodity. Some go through an extensive value chain process while

others products do not. The more extensive the value chain process is before the product

reaches the final customer the more likely it is that there will be a greater price difference

between the producer price and final consumer price. An extensive value chain process is

often observed in traditional export products like coffee, tea and tobacco.

The agricultural market in agricultural based countries can broadly be divided into two broad

categories the export market and the local market, which will be referred to as the staple

market. Even though the two markets have many similarities there are also many differences

between the two therefore speaking of a general agricultural sector would be wrong. The two

markets are to a certain extent not linked with each other due to obvious reasons. The

characteristic of the two markets in SSA will be discussed in the following sections.

a) The Staple Market

Local staple crops are those that are important in the diets of the people in that country. The

staple market remains are by far the most important agricultural sector in many agriculture

based countries. This mainly because staple crops often make up a large percentage of

household’s food expenditure and most of the farmland is used to grow these staple crops.

Households in poor countries are estimated to spend around 50 percent of their total income

on food, making household in these countries vulnerable to drastic price rises of staple crops

(World Bank 2008).

17

Many staple crops do not go through an extensive value chain process before they reach the

final consumer. It might therefore be suggested that the relationship between the price

received by the producer and the retail price does not differ significantly. This would be the

case if a farmer has access to a market because then he or she can sell his or her own produce

on the local market causing the producer price and retail price to be equal. However, often the

farmer cannot directly sell his or her produce on the local market. In agricultural based

countries small and medium-size traders and layers of intermediates are common in the staple

market and in the markets of other agricultural commodities. This causes the producer price to

significantly differ from the retail price.

Figure 7: Ghana’s maize markets ($ per 100 kilograms, 1998)

Han

dlin

g &

oth

er c

osts

Tran

spor

t

Com

mis

sion

& m

ktg

fee

Stor

age,

inte

rest

, los

ses

Who

lesa

le a

gent

fee

Who

lesa

ler p

rofit

Han

dlin

g, m

ktg

fee,

oth

ers

Tran

spor

t

Stor

age,

inte

rest

, los

ses

Who

lesa

ler p

rofit

Tran

spor

t, ha

ndlin

g, m

ktg

fee

Stor

age,

inte

rest

, los

ses

Ret

aile

r pro

fit

Tech

iman

who

lesa

le p

rice

Prod

ucer

pric

e Acc

ra re

tail

pric

e

Acc

ra w

hole

sale

pric

e

22,8

17,3

27

1,9

0,91,2

0,70,91,7

0,81,5

0,31,10,30,61,7

31

Source: Natural Resource institute, personal communication 2006. (World Development Report 2008)

Figure 7 clearly demonstrates the layers of intermediaries that influence the Accra retail price

in Ghana. The farmer has no direct access to the market in Accra, the capital city, if he or she

wants to sell his or her product this will go through traders and intermediaries. Along the way

each trader and intermediary tries to make a profit. The many middle men and intermediaries

that operate in this market cause the producer price to significantly deviate from the retail

price. In this process almost no or no value is added to the product it is the network of middle

men and intermediaries that cause the price to increase along the way. This however implies

18

that if the farmer would have the possibility to sell his or her produce directly on the local

market of Accra the producer price would rise significantly.

The staple markets, apart from being poorly integrated locally, are also often poorly integrated

internationally and regionally. If the staple market price is determined mainly by local

demand and supply then foreign prices of staple goods have little or no influence on the

domestic market price. This would imply that the staple market is more or less a closed

market. Most local staple crops are not traded internationally and at most sometimes

regionally (e.g. plantains). This means that most staple crops operate within a closed market.

Maize is the main staple crop for many SSA countries and unlike many other staples crops it

is traded regionally and internationally. A simple correlation test was performed between the

import price of maize flour and the producer price of maize from 1966 to 2005. This was done

in order to test to what extent the maize market was closed for foreign factors. The import

price of maize flour was used instead of the import price of maize because maize is hardly

imported into African countries whereas maize flour is. This does not weaken the analysis

because maize flour can be seen as a close substitute for local maize. A correlation coefficient

ranges from -1 to 1 in value where a coefficient of 1 indicates that the two variables move

perfectly in the same direction and visa versa. A correlation however says nothing about the

causality between two variables it only says something about the relationship between the

two. One would expect, however, that the correlation between the local producer price of

maize and the import price of maize flour to display a negative, zero or weak positive

correlation if local maize would operate in a closed market.7 Certain countries like Rwanda

have not imported any maize according to the official statistics and only since 1996 have they

imported some maize flour.

7 The problem with the FAO trade data is that when a country only imports a small amount of maize the price paid per tonne is always a lot higher than in other cases breaking a normal trend. The trade data therefore shows per country some conspicuous results which might influence the correlation.

19

Figure 8: Cameroon (% change in price)

-0.8

-0.4

0.0

0.4

0.8

1.2

1970 1975 1980 1985 1990 1995 2000 2005

Imported Maize Flour Maize

Source: FAO

Figure 9: Kenya (% change in Price)

-0.4

0.0

0.4

0.8

1.2

1.6

2.0

1970 1975 1980 1985 1990 1995 2000 2005

Maize Imported Maize Flour

Source: FAO

20

Figure 10: Malawi (% change in Price)

-0.4

0.0

0.4

0.8

1.2

1970 1975 1980 1985 1990 1995 2000 2005

Maize Imported Mazie Flour

Source: FAO

The simple correlation test between the percentage change of the domestic price in maize and

the percentage change in the import price of maize flour displayed different results per

country. Cameroon had a correlation of -0.04, Malawi 0.002 and Kenya 0.34. The results are

not exactly what was expected but nevertheless explainable. Kenya exports and imports maize

and maize flour for other countries nearby as Mombasa, Kenya’s main port, is the transit hub

for most of the regional export and import. Kenya is therefore better integrated in the regional

and international food market. Malawi’s maize market is closed from outside factors however

it does display a small positive correlation between the maize flour import price and maize

producer price. Even though Malawi is landlocked the maize flour price does sometimes

move in a similar direction as the local producer price when it has problems with producing

enough maize for self sufficiency. Figure 10 clearly demonstrates this fact as from 2002

onwards, 2002 together with 2005 were the years that Malawi experienced a drought, the

percentage changes in price move in similar directions. Cameroon’s maize market however

seems to be locked from outside imports as there is only a very small positive correlation

between the two variables. Rwanda as stated before barely imports maize products officially

and therefore one can conclude that the staple market is relatively locked. The conclusion that

therefore can be drawn that the staple market in Africa operates in a closed market. Only the

Kenyan staple market shows signs of being more open than other African staple markets.

21

When it comes to maize markets the concept of export and import parity is of importance.

Maize prices fluctuate between the import and export parity depending on the availability of

the domestic crop. If for example there is a surplus of maize on the local market then maize

must be priced competitively, at export parity, in order to attract export demand. If there is not

enough maize to meet local demand then the market operates at import parity which means

that the maize price encourages imports. Domestic price instability tends to be high in African

countries as the difference between the export and import parity is large, especially when

referring to land locked countries (Hazell, Shields, and Shields 2006). Many African

countries, especially land locked ones, have high transport/transaction costs and foreign

exchange constraints. This limits their capacity to operate on world markets when a domestic

shock takes place. Even though this has improved in many African countries over the last

couple of years many countries still have a limited capacity to handle domestic food price

shocks. Rural areas are also often remote and difficult to reach by modes of transport. This is

predominantly the case in Africa where it is estimated that on average less than 50 percent of

the rural population live close to an all-season road (World Bank 2007). In Benin, Malawi,

and Madagascar trader surveys indicate that transport costs constitute 50-60 percent of the

total marketing costs (Fafchamps, Minten and Gabre-Madhin 2005). Due to these constraints

the wedge between the import parity and export parity in Africa is large insuring that, to a

certain extent, the staple market prices are only determined by local factors and can fluctuate

drastically without triggering exports or import.

Higher demand for livestock feed, Bio fuels and growing populations have caused the demand

for staple crops to rise. However due to a number of obstacles in the African staple market

many countries will not be able to benefit from the higher international demand. The staple

market in SSA has high transaction costs, high product wastages and loses, poor market

integration, and limited access to credit. This will cause the staple market to remain isolated

from international market prices for quite some time. Prohibiting the staple producing farmers

from benefitting from the higher international demand for staple products (especially maize).

As long as this remains the case the farmer will not have an incentive to invest in staple crop

production as the supply is limited to local demand. Of course the danger of getting access to

world markets is that it might lead to a significant price rise of food products in SSA.

22

b) The Export Market

The export market is a lot smaller in third world countries than the staple market. Rwanda for

example is a country where it is estimated that 90 percent of the labour force work in

agriculture (The Economist Intelligence Unit 2007). There tea and coffee constituted on

average, from 1966 till 2005, 91 percent of the total exports.8 However when calculating how

much of the total farmland was used to produce these exports a staggering three percent was

the result. Only three percent of the total farmland was used to produce 91 percent of the

exports in Rwanda. This means that around ninety-six percent of the farmland was used to

produce agricultural commodities that are sold on the local market. For Cameroon bananas,

cocoa beans, coffee, cotton, pineapples and rubber constituted on average 80 percent of the

total exports from 1966 till 2005. To produce 80 percent of the total exports only 24 percent

of the total farmland was used. This means that around 70 percent of the farmland, in the

same time period, was used to produce agricultural goods that were sold domestically. These

examples clearly demonstrate that farming in Africa is orientated towards growing

agricultural commodities that can be used for own consumption or can be sold domestically

on the local market. Local staple crops constitute a large share of the farmland used to grow

agricultural commodities in Africa (see figure 11-14). In other words they dominate the local

agricultural production. Figures 13 and 14 also show that in the two landlocked countries,

Malawi and Rwanda, staple crops dominate the agricultural production more than in two

countries with access to the sea, Cameroon and Kenya.

Figure 11: Cameroon (% of farmland used for the selected staple and export crops)

0%

10%

20%

30%

40%

50%

60%

70%

80%

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Selected staple crops

Selected export crops

Source: FAO

8 Authors own calculation with FAO data

23

Figure 12: Kenya (% of farmland used for the selected staple and export crops)

0%

10%

20%

30%

40%

50%

60%

70%19

66

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Selected staple crops

Selected export crops

Source: FAO

Figure 13: Malawi (% of farmland used for the selected staple and export crops)

0%

10%

20%

30%

40%

50%

60%

70%

80%

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Selected staple crops

Selected export crops

Source: FAO

Figure 14: Rwanda (% of farmland used for the selected staple and export crops)

0%10%

20%30%40%50%

60%70%80%

90%100%

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Selected staple crops

Selected export crops

Source: FAO

24

Many of the export markets are characterised by many small producers especially when

referring to traditional export crops like coffee, tea and tobacco. There are usually only a few

large buyers that buy the crops and sell it in foreign markets. There are for example four

leading manufacturers who dominate the world coffee market Nestle, Procter and Gamble,

Kraft and Sara Lee who control around 40 percent of the world coffee market. This example

indicates the buying power that these four large companies have in contrast to the many small

sellers. In many export markets there are only a few companies that buy the raw agricultural

commodity from the farmer. Like the staple market in Africa the export is also characterised

by its many middle men and intermediaries; although probably not to the same extent as the

staple market as access to world markets is of importance for these export crops (World Bank

2007). A remote farm that is difficult to reach by modes of transport will, most likely, not be

able to grow export crops due to the fact that it is more expensive for buyers to get the

produce from this location. They will therefore concentrate of farmland which is more easily

accessible in order to reduce transaction costs. A clear example of this is in Zambia where

cotton is only grown in the East of the country where the ginners are located (Ianchovichina

and Lunstrom 2008). Also in Kenya the farmers located in the upper Eastland close to the

Nairobi-Mombasa main road are contracted to grow French beans for export (Owuor 1996).

This is done in order to reduce transactions costs; other farmers can grow cotton or French

beans but they have no way to sell it.

The producer price of export crops should in theory move in a similar direction as the export

price of these products if the market works efficiently. Export crops contrary to staple crops

are influenced by foreign factors therefore it is expected that the producer price is influenced

by the export price of the product. In order to test this a simple correlation test was performed

between the log of the export price and the log of the producer price of the selected export

commodities. The results of these correlation tests are displayed in the table 1. As can be seen

in the table the producer prices have the tendency to move in a similar direction as the export

prices of these commodities, hence the strong positive correlation. For Kenya the FAO used

for coffee and tea producer prices the auctions prices which will explain the high correlation

between the export price and the producer price.

Some commodities however did not display a strong positive correlation. For example the

producer price of tobacco was negatively correlated with the export price for both Cameroon

and Malawi. For tobacco no explanation could be given for the negative correlation. The fact

25

that tobacco has a negative correlation for both countries might however indicate that the

market structure of tobacco is different than for other export products. Pineapples for Kenya

also displayed a negative correlation. For Kenya the export price of pineapples canned was

used because this is how Kenyan pineapples are mainly exported. This might be an

explanation for the negative correlation as a manufactured product (pineapples caned) does

not have to be positively correlated with the producer price of pineapples. For Rwanda tea

also displayed a negative correlation. However one important thing to note is that the

producer prices of the FAO for Rwanda from 1991 to 2005 are rural market prices and the

producer prices in 1994 are entirely estimated. They might therefore contrary to the producer

prices of other countries not reflect the changes in the incomes of the farmer. The correlation

between the producer price and the export price of tea before 1991 did however display a

strong positive correlation of 0.78. Most correlation tests do however confirm that export

producer prices do have the tendency to be influenced by foreign factors unlike staple crops.

Table 1: correlations between producer and export pricesCameroonCocoa 0,54Coffee 0,85Cotton 0,59Natural Rubber 0,74Tobacco -0.09KenyaCoffee 0,96Maize 0,65Pineapples -0,32Tea 0,85MalawiSugar 0,14Tea 0,63Tobacco -0,18RwandaCoffee 0,66Tea 0,03Source: Authors own calculations with FAO data.

An important thing to note is that on average the export producing farmer earns more per

hectare than the staple producing farmer. (See figure 15, 16 and 17)9 This is the case for every

country in this analysis. So how come that even though, on average, export production is

more profitable most farmers still produce staple crops? The first possibility is that they do

9 For a detailed explanation of how these revenue indexes are constructed skip to section 6b.

26

not earn enough to survive when they produce export crops. Suppose a farmer has half a

hectare of land he could then choose to produce an export crop which according to the 2005

export index of Cameroon would earn him 286 dollars per year. Or he could produce staple

crops which would earn him 189 dollars per year. The staple crops however can be used for

own consumption. The farmer therefore does not need to go to the local market to buy the

amount of staple crops he needs in order to survive. Now consider the likely scenario that

staple crop prices rises rapidly then the farmer could be better of producing his own food

instead of producing export crops. Switching to export crops might be a great risk to take

especially when the farmer only has a small plot of land.

Figure 15: Revenue indexes of Kenya

0500

100015002000250030003500400045005000

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

$/Ha Export revenue index

Staple revenue index

Source: Authors own calculations with FAO data

Figure 16: Revenue indexes of Malawi

0

200

400

600

800

1000

1200

1400

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

$/Ha export revenue index

staple revenue index

Source: Authors own calculations with FAO data

Figure 17: Revenue indexes of Rwanda

27

0

200

400

600

800

1000

1200

1400

1600

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

$/Ha export revenue index

staple revenue index

Source: Authors own calculations with FAO data

However this scenario does not seem very probable and might only be applicable to a small

group of farmers. It is more likely that transport cost and lack of capital needed for switching

to these export crops prohibit the farmer from switching. It is possible that only farms that are

located close to major cities or centres of commerce or have good accessible roads or other

modes of transport nearby have the possibility to produce and sell export crops. Other more

remote areas might be able to grow export crops however they do not have the possibility to

sell it due to transportation cost or other constraints. Considering the fact that good all-year-

round useable roads are often scarce in African countries the importance of market access is

probably a key in understanding why export crops are not grown so extensively.

It might also be the case that a farmer has potential markets access however he currently

grows staple crops and does not have the capital to make a switch to export crops (cash

crops). The inability to lend or save might be the only constraints prohibiting this farmer from

growing cash crops. On many of the small farms in Africa families balance on the verge of

survival. Saving means cutting back on current consumption in order to spend more later.

However when you are balancing on the verge of survival saving is not an option.

Figure 18 displays the differences between the real price of maize in the main food markets in

Cameroon. Cameroon, like Malawi and Kenya, is a big country and therefore has many large

food markets.10 A farmer growing maize near to Doula will probably receive a higher price for

his or her maize than a farmer located near Garoua. The monthly real prices of maize also

display the heavy fluctuation that the staple market price experience. Considering the fact

10 Rwanda is a lot smaller than the other countries in the sample. It therefore only has one main food market and that is the capital Kigali.

28

that, given the current exchange rate, 1 Euro is equal to approximately 655 CFA Francs the

price difference between the local markets is a matter of cents per kilogram. However this

would be a wrong way of looking at the differences in price. When the per capita income of a

country is low and farmers often earn a lot less than the per capita income these price

difference can make a world of difference. Also when looking at the price difference as a

percentage of the price or per tonne then the differences are large. The local price fluctuations

might therefore be another reason why farmers in Africa might prefer to grow their own food

product instead of being dependent on the local market which is somewhat unstable. That the

value added and income of a commercial farmer is higher is probably not enough incentive

for the farmer to take the risk of growing commercial crops given the instability of the

domestic food market.

Figure 18: Cameroon: The retail price of maize in real local currency per kilogram.

050

100150200250300350

YaundéGarouaDoula BamendaBafoussam

Source: National Institute of Statistics

Institutional factors might also influence the export market. In Malawi market liberalisation,

which reduced the differential protection of large estates in the mid 1990’s, enabled

smallholders to produce cash crops. Small holders, who constitute 85 percent of the

population, are now producing 70 percent of the burley tobacco, which is Malawi’s main

export crop. (The Economist Intelligence Unit 2008c). The earlier given examples of Vietnam

and Uganda also show the importance of institutions/ government regulation in determining

the outcome of a market.

The benefits of switching to export crops instead of producing staple crops, according to the

indexes constructed for Kenya, Cameroon, Rwanda and Malawi, are huge. (See figures 15,

29

16, 17 and 19) The amount revenue an export farmer in 2005 received for his produce is on

average 4 times as high as the amount of revenue a staple farmer receives for his produce in

the same year. The export index is almost consistently higher than the staple index over time.

Proving that producing export products is more profitable over the course of history. The

amount received for export produce from 1966 till 2005 is on average around 4.4 times higher

than the amount received for the staple produce. This gap increases and decreases over time

however the export index remains consistently higher than the staple index in all countries for

all years with the exception of Cameroon. Indicating that an export farmer consistently

receives more revenue for his or her produce than a staple producer. The huge difference

between the two indexes should have stimulated many farmers to make the switch to cash

crops. The fact that this is not happening on a large scale raises question as to why this is not

the case?

The amount of farmland used for exports increased between 1966 till 2005 in all four

countries. But for the countries that had the biggest difference between the two indexes Kenya

(on average 8.3 times as high) and Malawi (on average 5.4 times as high) the amount of land

used for growing export crops increased in relative terms by 170 and 163 percent respectively

over this time period. In Cameroon, where smallest difference between the two indexes was

observed (on average 1.28 times as high), the amount of land used to grow export products in

relative terms increased by around 36 percent over the same time period. Cameroon was the

only country, in this sample, where for some years the export index was below that of the

staple index. This caused many producers to switch to staple crops (see figure 19). Rwanda

had a difference between the two indexes that was in between that of Cameroon and Malawi.

There the export index was 2.75 times as high as the staple index. Therefore the farmland

used to grow export crops expanded in relative terms by 106% over the same time period.

Indicating that as the benefits between producing cash crops instead of producing staple crops

becomes larger more farmers are willing to produce the cash crop. Nevertheless in two

countries, Cameroon and Rwanda, the amount of land used to grow staple crops actually grew

faster than the amount of land used to grow export crops on.

Table 2: The average revenue per hectare and the percentage increase in farmland from 1966 to 2005

Cameroon Kenya Malawi RwandaAverage staple revenue

343.2 252.42 137.4 366.7

30

Average export revenue

414.4 2004.7* 710.6 457.4

% increase in staple farmland

36% 170% 163% 106%

% increase in export farmland

70% 51% 80% 144%

% increase in total farmland

56% 61% 85% 142%

* For Kenya the auction prices for tea and coffee were used as the producer price by the FAO this will explain the high export revenue.

Figure 19: The amount of land used to grow export crops with fluctuating indexes

Cameroon

0

200

400

600

800

1000

1200

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

staple revenue index

export revenue index

Export land (per 1000hectares)

Source: Authors own calculations with FAO data

The export crop market might seem lucrative when looking at the indexes, but this is hardly

the case. The real price for their export produce has in many cases declined or stayed constant

when comparing it to what farmers used to receive in the 80’s.11 The real producer price for

export crops is more volatile than the real staple price bringing along with it a large amount of

insecurity for the farmer. This makes the export market a lot less lucrative than what it looks

like at first sight. However it remains the case that the income of an exports producing farmer

is, according to the indexes constructed by the Author, higher than that of staple producing

farmer. This by itself should be an adequate incentive for a farmer that has the market access

and capital to switch to do so.

11 FAO data

31

It is important to note that when competition in the staple crop market becomes less intense

because more farmers start shifting to export crops men might see a significant rise in the

price of staple crops. The other possible scenario is that import of staple crops will happen

more frequently as the country cannot for fill its own demand. However given the wedge

between import and export parity in most Sub Saharan African countries a higher price

scenario without a massive increase of imports seems more probable. All in all the staple crop

market will become more profitable for farmers and more interesting for investors. In

Vietnam, for example, the massive shift of subsistence farmers to exports crops also caused

subsistence farmers who did not make this shift to experience an income increase due to the

fact that competition had decreased in this sector (World Bank 2007).

6) The Cournot model

a) Cournot model implications for a various number of competitors

It is the structure of the market that determines to a very great extent the price of a product.

Market structure on its turn is determined by many factors. Examples of these factors are the

characteristics of the product sold, government legislation, and the amount of buyers and

sellers. The agricultural market has a unique structure which helps understand how the prices

of the agricultural commodities are determined. The agricultural sector is in economics the

closest example of a perfectly competitive market. This unique market structure has four

characteristics which are also displayed in the agricultural sector.

The first characteristic is that products are homogeneous, which means that every product is

identical. When two different farms produce the same commodity, for example pineapples,

then it is very likely that one pineapple does not significantly differ from another pineapple

and is therefore homogenous. With different agricultural crops this is not the case. This

ensures that not all farmers are operating in the same market due to the fact that they produce

differentiated agricultural commodities. Important to note is that products that have

differentiated quality are also not homogeneous. Coffee for example has different grades of

quality, Robusta beans are not the same an Arabica beans, they are therefore considered to be

differentiated products.

32

The second characteristic is that there are many producers each producer representing a very

small part of the overall supply of the good. This is the case in the African agricultural sector

where small farms make up most of the industry. Even though there are different markets and

many different agricultural commodities grown the amount of farmers producing the same

commodity is still very large.

The third condition is that the producer is a price taker. This means that the quantity a single

farmer produces does not influence the market price, implying that each farmer has no market

power due to the homogeneity of the commodity and the heavy competition. The population

that is economically active in the agricultural sector for Kenya is estimated to be 26,552,000.12

This is basically the agricultural labour force this of course includes farmers, wage workers

and unpaid workers. This however demonstrates how immensely large the agricultural sector

is in just one country, Kenya. There are also many farms as both the staple production and

export production is dominated by smallholders in Kenya. This means that there are many

producers of agricultural commodities in Kenya ensuring that the third condition holds in

countries like Kenya. The heavy competition on the world market also ensures that the third

condition holds for export crops.

The fourth and last characteristic is that there is free entry and free exit for farmers. This last

characteristic is also to a certain extent the case in most agri-based economies. Any farmer

could potentially switch his production to another crop in the long run. However, this switch

in production is not always completely free. A producer might have to incur certain costs for

switching his production to another crop and certain barriers might prohibit the farmer from

entering the market. On the other hand exiting the market is almost always free. The

implication of the last characteristic is that there will be zero economic profits in the long run

for all farms. Due to the last characteristic farmers will enter when it is profitable for them to

do so and exit if they make a loss. This will lead to the result of zero profits for all farmers in

the long run. At zero profits the market is in equilibrium as there are no incentives for farmers

to enter or exit the market.

Both the export producing farmer and the staple producing farmer operate in a perfect

competitive market. There are however known differences between the two markets in which

12 FAO 2006 estimate

33

the farmers operate. The exporting producers have to compete with world wide competition.

Staple producer operate in the domestic market and most likely only have to compete with

domestic producers. This is due to the fact that local staple markets often seem to be closed of

from staple imports. Also the export farmer usually only has a few large potential buyers. The

staple farmer can sell his product most likely to many different potential buyers and can also

use his produce for own consumption. Therefore the amount of potential buyers might be

higher in the staple market than in the export market. These differences however will not lead

to different results in the price received for the produce as the analysis will show.

As an example of an export market the coffee market will be used. There are an N amount of

producers that grow coffee beans. There are however only two firms that buy the coffee beans

firm Y and firm Z. The producers engage in Cournot competition, that is, they compete on the

amount the quantity they produce which in this case is simultaneously and independently

determined. Each farm interacts strategically with the other farms each time choosing the best

reaction to the quantity that the other farms will produce. The result of this strategic

interaction leads to numerous amounts of possible quantities the farm can produce, given the

reaction of the other farms, giving different prices. It is assumed that the farm is profit

maximising. This implies that he or she will choose a quantity, given the other farms best

reaction, which will maximize his or her profits. It assumed that farmers make this decision

simultaneously and independently. This means that all the farmers make their decision as to

what quantity to produce at the same time without knowing what the other farms will do.

The formulas 1.8, 1.9, 2.0 and 2.1 are the four main outcomes of the Cournot analysis. (How

these four formulas are obtained can be seen in the appendix 1 with the necessary

explanation) The first formula describes how the amount of firms (N), the marginal costs (C),

and two constant parameters (A and B) influence the quantity produced by the individual

coffee farmer in the Nash equilibrium (q). All the individual coffee farms are assumed to be

symmetric they will therefore all produce the same quantity in the Nash equilibrium. The

Nash equilibrium is the equilibrium in a noncooperative game where each player has no

incentive to change its strategy given the strategies of his rivals. The formula for the total

quantity produced (Q), the Price (P) and the total profit (Π) in the Nash equilibrium is given

by the formulas 1.9, 2.0 and 2.1.

(1.8)

34

(1.9)

(2.0)

(2.1)

The basic conclusion that can be derived from equation 2.0 is that when N (the number of

farms) rises the price gets closer to the perfect competition result where P (price) = C

(Marginal cost). This analysis shows that as the number of farms growing a certain crop rises

the price gets closer and closer to the marginal cost of producing that crop. The amount of

producers (N) is very high in most African staple markets and given the high global

competition for virtually all export crops the agricultural market in SSA behaves like a perfect

competitive market. Which implies that farmers earn zero profits in the long run as the price

they receive for their produce is equal to the cost made to grow the produce.

The farmers in third world countries often engage in perfect competition. However the buyers

of these agricultural products are often large firms with significant market power. In this

analysis there are many producer of agricultural products to two large firms, firm Z and firm

Y. The producers, basically farmers, engage in perfect competition with each other. As can be

seen in the analysis of the Cournot model this leads to the farmers selling their produce at

price equal to their marginal cost. It is assumed that firm Z and Y are the sole buyers of the

farmers produce. The firms buy the produce at the marginal costs (C) of producing that good

by the farmers. The firms Z and Y engage will most likely engage in price competition with

each other for which the Bertrand model is used. The problem with using the Bertrand model

is that without certain assumption any price deviation between the two firms will lead to an

immediate and complete loss of demand for the firm with the higher price. This results in an

equilibrium in which both firms charge a price equal to the marginal costs. Considering the

fact that in reality most firms engage in price competition (Bertrand model) and most of them

do not charge prices equal to marginal costs much criticism has been made on this model.

However if certain minor adjustments are made to the model like the assumption of capacity

constrains or differentiated products then the firms are able to set prices higher than the

35

marginal cost and earn profits.13 However for simplicity we assume that firm Z and Y engage

in Cournot duopoly competition. This does not necessarily weaken the analysis. Equation 1.8,

1.9, 2.0, and 2.1 can be used to derive the results from the duopoly situation in a Cournot

model. Basically the only procedure that has to be performed is that N=2 is filled into to these

formulas in order to get the Cournot duopoly results. When this is done the duopoly versions

of the equations are obtained as displayed below.

(2.2)

(2.3)

(2.4)

(2.5)

Important observations can be made when looking at these formulas. The first important

observation is that the quantity produced per firm is greater however the total output is less

than that produced under perfect competition. As N increases the equilibrium price will

approach C, which represents marginal costs, however due to the fact that there are only two

firms the price is likely to be higher than the marginal cost. When P* is lower than marginal

cost, when N equals two, then one or both firms will exit the market. Let us however assume

that the demand function facing these two firms is equal to P = 100 – 2Q and that MC = 4.

Then it is therefore known that A = 100, B = 2 and C = 4. This means that the Duopoly

output QD and price PD are: QD = 32 PD = 36. However when N increases to 50 firms then the

price falls to approximately 5.9 as and total output rises to 47. Most importantly the total

profit falls as the number of firms rises. In our case the profit falls from 512 per firm to 1.77

per firm. Clearly indicating that as the number of firms in an industry rises the price starts

approaching the marginal costs and profits per firm fall. When applying this model to the

farms and the large firms that buy the farms products it can be seen that the profit is not made

by the farmers who receive a low price for their produce. The firms that make the highest

profits are the firms that buy the products from the farms and then sell them to consumers in

other markets. One can question the ethics of this especially considering the fact that many of

these farmer are balancing on the verge of survival. However, it must be remembered that it is

to a certain extent the market structure that leads to this result13 A capacity constraint basically means that if a firm charges a lower price he does not have the capacity to supply the market even if he sets a lower price. This will prohibit both firms from setting a price equal to marginal costs as both firms do not have the capacity to supply the whole market.

36

b) Horizontal mergers and the Merger Paradox

Then the question arises why the farms do not merge to form greater farms and increase their

market power. The answer to that question lies in the merger paradox. The merger paradox

states that it is quite difficult to construct a simple economic model in which there are sizable

economic gains for the farms horizontally merged except when they merge to become a

monopoly. When farms are engaged in perfect competition it can easily be shown through the

Cournot model that a merger does not easily lead to gains for the merged firms unless a large

fraction of the farms merge.(For mathematical workout and numerical example see appendix

2). Even if that were possible the monopolist or large firm must now be able to prohibit other

producers from entering the market in order to maintain such an obtained privileged position.

Given the structure of the agricultural market this is not easily done.

Often in order to make a merger in Cournot competition profitable a StackleBerg model (in

which Cournot case a first mover advantage is obtained) is introduced or efficiency gains for

the merged farms. However neither of these two additions to the Cournot merger model is

applicable to small scale labour intensive farms in third world countries. The Stackleberg

model is not very useful due to the fact that there is no/almost no first mover advantage in

farming given the perfect competitive structure of the market in which it operates. The

farmers grow the crops at the same time and each farmer can sell its entire output at the given

market price. Therefore credibly committing to a certain output will most cases not influence

the output decision of other farmers.

Efficiency gains in farming are also hard to achieve in third world countries. Merger

efficiency gains in the Cournot model basically mean lower cost (usually referring to marginal

cost) due to the horizontal merger. However in most third world countries agriculture is

labour intensive, as capital is scare. Most third world countries have a comparative advantage

in labour intensive products as labour is cheap relative to capital. Therefore, assuming that a

merged firm will continue to employ only labour and no extra capital, it will be hard for

merged farms to lower their marginal costs after a merger.

Now assume that there two kinds of coffee beans, Arabica beans or Robusta beans, that

farmers can produce. The choice of which bean to produce is not entirely up to the farmer to

37

decide. The Arabica beans are more delicate they require a cool subtropical climate, lots of

moisture and rich soil. They are extremely vulnerable to cold and bad handling and are also

subject to attacks of various pests. They must also be grown at a higher elevation of 600 to

2000 meters above sea level. Robusta plants are stronger and are able to grow at a low altitude

of 200 to 800 meters above sea level. They are also less vulnerable to pests and rough

handling and yield more per hectare14. The production conditions alone will lead to a natural

selection of farmers who can and farmers who cannot grow the Arabica beans. This gives

Arabica farmers the possibility to exclude other farmers from producing Arabica beans due to

the nature of the crop. Farmers who want to grow Arabica but have a farm located at lowers

altitudes or in areas prone to many pests will be prohibited to grow Arabica beans

successfully. If the Arabica farmers would be able to form a cartel then they could have the

possibility to charge a price above the marginal cost of producing the coffee beans. This type

of exclusion may lead to speciality markets that offer an alternative higher priced product. For

example a Tanzanian coffee smallholder association (KILICAFE) produces Kilicafe a

speciality high quality coffee. This association is made up of around 8000 smallholders in

Tanzania’s three Arabica growing areas (Kilimanjaro, Mbnga and Mbeya).15 This initiative

gives these farmers the opportunity to receive higher prices, promotes better quality coffee,

and gives credit facilities. However the speciality coffee market is relatively small and only

accounts for 6-8 percent of global consumption (Baffes, Lewin, and Varagis 2005).

Nevertheless as stated before if exclusion of other farmers is possible then farmers have a

possibility to charge a higher price than marginal costs for their produce.

How does this work in the previously used Cournot model. In the speciality coffee situation

the coffee producer can exclude other farmers from entering. They therefore have the

opportunity to form a monopoly or near monopoly of the Arabica coffee production of

Tanzania. The cooperative also insures the high quality, reducing transaction costs, and helps

farmers make efficiency gains. Both the monopoly position and the efficiency gains provide

the KILICAFE farmers in Tanzania the opportunity to receive a higher price for their produce

then would otherwise be the case.

7) Price pass-through theory

14 On average Arabica plants yield 500-1000 kg of Arabica beans per hectare while Robusta plants yield on average 1200 kg of Robusta beans per hectare. 15 Kilicafe

38

The way the price moves through the different levels of the agricultural industry is of

importance for the analysis of this paper. The Exchange rate pass- through (EPT) analysis of

Bowen, Hollander, and Viaene (Applied International Trade Analysis, 1998) will be used to

discuss the price through effect in the agricultural markets.

A change in the exchange rate between two countries can have three different effects on price

of the product in the foreign market. If the EPT is complete, which means that the elasticity of

the domestic currency price with respect to the exchange (ε) is equal to zero, then the firms

will maintain their margins on foreign sales and pass any exchange rate change on to the

consumers. If the EPT is incomplete, ε is then greater than zero, then the exporter will absorb

part of the exchange rate change in their margins. When EPT is incomplete foreign firms will

experience decreasing margins when the exchange rate appreciates and increasing margins

when the exchange rate depreciates. With perverse EPT the reverse would be true. The model

used by Viaene, Hollander, and Bowen assumes that there is a linear demand for a single

homogenous commodity. It also assumed that the firms adopt a Cournot strategy, the amount

of firms is fixed, and that the markets are segmented. This is done so that the model only

focuses on the price and quantity in the foreign market. There are n amount of domestic

producers who supply the foreign market. The foreign markets also has n* producers of its

own. The price of the commodity in the domestic market is P, the exchange rate is denoted by

e, and the price of the commodity in the foreign market is denoted by P*. The domestic

producers (n) are assumed to be interested in the domestic currency price of the commodity in

the foreign market i.e. p = ep*. The marginal cost of the domestic producers (n) are c and the

marginal costs of the foreign producers (n*) are denoted by c*. When walking through the

basic steps of a Cournot model the key result of the EPT model is obtained.

(2.6)

This equation basically states that the EPT is determined by two factors. The first factor that

influences the elasticity of the domestic currency price with respect to the exchange rate is the

relative number of exporting firms. This first factor shows how different market structures

influence the EPT. If the amount of domestic producers is large relative to the number of

foreign producers then the first expression will approach 1. Foreign producers are then price

takers and the EPT will be complete (ε = 0). If however the amount of foreign producers is

39

relatively large compared to the amount of domestic producer then the first factor will

approach 0. Which would imply that the domestic producers are price takers as the elasticity

of the domestic currency price with respect to the exchange rate is equal to 1. If a domestic

firm has a monopoly position in the foreign market (n=1 and n*=0) the EPT is incomplete (ε

< 0). Basically the market structure influences the extent to which firms adjust prices to

exchange rate changes. The second factor that influences the EPT is the ratio of marginal cost

to the domestic currency price of exports.

The EPT analysis displays how firm prices react to shocks. This does not have necessarily

have to be a change in the exchange rate. The EPT analysis shows that the more competitive

the market is, which basically means the greater the number of firms, the more complete the

price through becomes. Therefore if we apply this result to the producers of coffee, middle

men, distributors and large international buyers one can see how the price passes through in

this market. In the agricultural markets the producers do not have the market power but the

large international companies or possibly the middle men or distributors. It now assumed that

the international price of the coffee rises. How would the price through operate in such a

context? The large international companies do not form a monopoly but they do however

dominate the coffee market, which means that they have market power. This market power

will allow them to incompletely transfer this international coffee price rise. This will allow

them to increase their margins when international prices rise and decrease their margins when

prices fall. Considering the fact that coffee producers operate in a perfectly competitive

market a price shock for the producer will pass through completely to the producer price.

Leibtag, Nakamura, Nakamura and Zerom have researched the price pass through of coffee in

the U.S coffee industry (Cost pass-through in the U.S coffee industry, 2007). They estimated

that the average manufacturing price per ounce of coffee was 23 cents in 1997.

Approximately 11 cent per ounce was spent on coffee beans, 3.5 cent on labour and other

inputs and 9- 10 cents constituted the gross margin. In 2002 the average manufacturing price

of coffee per ounce dropped to 17 cents. In 2002 only 4 cents per ounce was spent on coffee

beans, 5.5 cents on labour and other inputs and 7-8 cents constituted the gross margin. These

figure show that the gross margin tends to be low when the price of coffee beans is high and

particularly high when the coffee bean price is low. Leibtag, Nakamura, Nakamura and

Zerom did more calculations for consecutive years and found evidence that international

40

coffee buyers incompletely transfer coffee bean prices into their margins, hence an incomplete

price transfer.

8) Empirics

The Cournot model gives a theoretical description of how the agricultural market should work

in theory. This model is helpful for understanding the African agricultural market. How the

market operates in practice can deviate from this simple model. Therefore a model was

constructed to test empirically the effect of producer prices on the income of farmers.

a) The Data

The construction of a model starts with the selection of data. Data selection is always a

difficulty for a researcher. It is often difficult to find relevant and reliable data and once it has

been found there has to be enough in order to use for statistical analysis. Even when drawing

data from reliable sources like the FAO and the World Bank one must always be cautious and

not just use the data without thoroughly checking it. A too high to too low figure, breaking the

obvious trend, is often there for a reason. Why the inconspicuous data is there is for the

researcher to find out. The researcher could possibly replace it with more reliable data or

correct it for possible obscurities like a currency change. There is also always the possibility

that the data is correct and then the story behind the figure becomes of more importance.

So too has this paper had the necessary obstacles with data, some still unresolved. The limited

data caused for an automatic selection of countries that could be researched. Eventually only

four countries remained of the original eight SSA countries selected due to data constraints.

The first problem encountered pertains to poverty data which is almost non existent for Sub

Saharan Africa (SSA). This is unfortunate because it is this region where most of the poor live

and most of the population is still heavily dependent on agriculture. These figures are

therefore for the research of this thesis of vital importance. But the obstacle of almost non

existent poverty data made matters just a little bit more difficult. The World Bank has a

database called POVCAL which uses household survey data to estimate the poverty.

Shaochua Chen and Martin Ravallion, (How have the World’s Poorest Fared Since the Early

1980s?, 2004) the constructors of this database, stress heavily that especially the Sub Saharan

41

African data should be interpreted with much caution. Where many Asian and Latin

American countries had on average eight poverty surveys since 1981 the highest amount of

poverty surveys found in Africa was for Cote d’Ivoire which had six surveys. This would be a

ridiculous low amount of surveys to base poverty estimations covering a time span of 23 years

on. The author sent a mail to the World Bank asking about the availability, comparability and

reliability of the poverty data coming from their POVCAL database. The World Bank sent

back a document with a list of countries with questionable poverty data. (For the document

see appendix 4) What became painfully clear with this document is that there are many

problems with the poverty data especially when referring to SSA. The poverty data seems to

be inconsistent over time, prohibiting the author from using the already limited data in the

analysis.16

The data used for this thesis came from two main sources the FAO and the World Bank. Due

to a lack of poverty data the author searched for other variables which was less restricted and

could be used as a proxy for agricultural income. The variable that was eventually used as

proxy for the farmers income was agricultural value added per worker from the World Bank.

This variable measures the productivity of the agricultural sector (ISIC divisions 1-5) minus

the value of intermediate inputs. This variable however is calculated without taking into

account depreciation of assets or the depletion of natural resources. The agricultural sector

(ISIC divisions 1-5) consists of forestry, hunting and fishing, livestock production and

cultivation of crops. However cultivation of crops, by far, dominates this variable as it is the

largest component especially when referring to SSA countries. Due to the nature of the

agricultural sector in the SSA countries the staple crop cultivation will dominate the

agricultural value added per worker.

b) The construction of the revenue indexes

The two main variables, EX and ST, are used extensively for the analysis in this thesis. The

variable EX is an index which estimates the revenue farmers gain from growing export crops

on one hectare of land per year. The other main variable is ST which is the revenue per

hectare of land a farmer gains when growing staple crops per year. The construction of a

16 Problems with the poverty data has been the main reason why the new $1.25 a day poverty line headcount ratio has been omitted from the world development indicators (WDI) for over a year now. However POVCAL data has been used for new poverty analysis preformed by the World Bank. The reliability of such reports should strongly be questioned

42

staple or export revenue index was done with a great deal amount of caution and was

extremely challenging to construct. The goal of the revenue indexes was to display the food

price/revenue changes that the domestic producers experienced from 1966 till 2005.

The FAO databases provided data on export, import and producer prices that date back to the

1960’s. The producer price is the average price of an individual commodity, comprising all

grades, kinds and varieties, received by farmers when they sell their own product at the farm

gate or first-point-of-sale. However producer prices in US dollar only dated back to 1991, in

the case of Brazil only to 1993. Luckily the archive of the FAO database has producer prices

in local currency from 1960 till 1991. 1991 is the year that overlaps between US producer

price database and the archive database. The local producer prices were converted into US

dollars, using World Penn table exchange rate, and then divided by the GDP deflator of that

country in order to obtain real producer prices. Currency changes had to be converted using

the conversion rates which were obtained from the FAO. In the process a series was produced

for Rwanda, Kenya, Malawi and Cameroon from 1966 till 2005, which was then the latest

figure, of the producer prices per commodity in real US dollars.

In order to make the index comprehensible and not too complicated a selection was made as

to which products would be put into the index. These indexes were constructed in such a

manner that it would include 50% of the total land used for agricultural production within a

country. This threshold was chosen in order to insure that the indexes are representative for

the country’s agricultural production. Why 50% percent and not 60% or 70%? This is because

there are always important export and staple products so in the beginning it is easy to reach a

certain percentage. But when the benchmark of around 50% of the farmland is reached

smaller commodities have to be added in order to reach a higher percentage. Therefore this

threshold was chosen because if all commodities would be added to the index it would be too

large and obscure. So basically the index contains the main commodities of a country, less

important commodities were omitted from the analysis. This simplifies the revenue index and

does not influence the fact that the revenue index is representative for the country as whole.

The export index had to fulfil the requirement that at least 50% of the total quantity exported

would be represented by the chosen sample. Given the long time span of the index, covering a

period of nearly 40 years, it was rather difficult to find a selection of products that met this

requirement. The world market for traditional agricultural export goods like coffee, cocoa

43

beans, cotton, tobacco and tea is very turbulent. The price of an agricultural commodity is

determined by the supply and demand of that good. Demand for most traditional agricultural

export commodities until recently has grown relatively slow never outpacing the ever

increasing supply (World Bank 2008). This has led to lower prices for many traditional

agricultural products over the years. African economies, often being heavily dependent on one

or more of these commodities, have often seen there export incomes fall. This should have

been a strong incentive for African countries to diversify their agricultural exports away from

the traditional exports as prices were plummeting; alas this was not the case. It has only

caused the importance of traditional exports in many African countries to slightly fall as the

share of untraditional agricultural good has grown. However in most African countries the

traditional crops still dominate the export production. For the selected African countries an

export index could be constructed that had on average 50 percent of the total agricultural

quantity exported in it with using at most six agricultural products.(see Table 3)

Table 3: The selection of agricultural commodities and their importance per countryCameroon Kenya Malawi Rwanda

Selected export commodities

1. Cocoa Beans2. Coffee(green)3. Cotton lint4. Natural rubber5. Bananas 6. Tobacco

1. Coffee(green)2. Tea3. Maize 4. Pineapples

1. Tobacco2. Tea 3. Sugar

1. Coffee(green)2. Tea

Selected staple commodities

1. Sorghum2. Maize

1. Maize2. Sorghum

1. Maize2. Cassava

1. Plantains2. Beans

44

3. Plantains4. Cassava5. Rice

3. Wheat 4. Cassava

3. Potatoes 3. Sorghum4. Potatoes5. Maize6. Cassava

Average % of total quantity exported

79,4% 54,4% 61,7% 92%

Average % of total farmlandExport 25.9% 7.4% 5.0% 3,5%Staple 35.8% 45,6% 57,8% 80,5%Total 61,7% 53.0% 62.8% 84.0%

The selection of staple crops per country was based on the FAO country profiles. These

profiles give a per capita daily calorie intake from a group of selected food items. The main

food items from this profile represented the most important staples crops of a given country.

Later, when looking at the farmland used to produce these certain crops (also FAO data), the

importance of these staple crops was clearly displayed. As every time the selected staple crops

dominated local agricultural production as was be expected. (See table 3)

The real producer price per ton of the chosen goods was then multiplied by the yield per

hectare of that good. This was done in order to estimate the average revenue a farmer would

receive for grown a certain crop on one hectare of land. The yield per hectare would also

embody the technological progress or efficiency gains that have been experienced in the

agricultural sector in a given country. Of course yearly fluctuations in weather conditions

would of course also affect the yield per hectare but only in the short run. An upward trend in

the yield per hectare could display agricultural innovation and efficiency gains. Some of the

agricultural goods that where used in the index have displayed an upward trend in the yield

per hectare over the 39 years. This basically indicates that efficiency gains have been made

for these agricultural goods. These efficiency gains, however, have been more clearly

displayed in the export farm sector than the staple sector. The yields of all crops are volatile

and can fluctuate heavily from year to year. This is of course due to the fact that most of the

crops of smallholders, who often dominate local production in African countries, depend on

rain for irrigation. Some crops like tea display a clear upward trend in the yield per hectare for

Malawi, Kenya, and Rwanda. While other crops like coffee display in two of the three

countries a downward trend in yield. This downward trend in coffee yield was the results of

the growing importance of high quality when referring to coffee beans.

45

The real local producer price per hectare of land was then multiplied by the amount of

hectares that where used to produce that good divided by the total hectares used to produce

the goods that were in the index. This was done for every good so that the goods that are of

more important would be weighed heavier than goods that are of less importance for local

farmers. At first both staple crops and export crops were combined into the same index. The

staple crops through this weighing method would dominate the index as they are grown more

extensively than export crops in the selected countries. Therefore the indexes were separated

and split up into two indexes a staple index and an export index.

(2.7)

The EX can be described by the following formula. The index is the weighted average of the

revenue received by a farmer per hectare of farmland where export crops 1 through N are

grown in country j. P1 is the producer price in US dollars received by the farmers for

producing one tonne of export crop 1. Y1 is the yield per year of export crop 1 in tonnes per

hectare. The result of this multiplication is the producer price of export crop 1 per hectare of

land. The total amount of land, in hectares, used to grow export crop 1 is denoted by l1. The

revenue per hectare of export crop 1 multiplied by the total land used for export crop 1 gave

the total producer revenue of export crop 1 in country j. This was then divided by L ex this is

the total amount of land used to grow export crops 1 through N. This was done in order to

take into account the importance of the export crop in the local production. This was done for

all the selected export crops (1-N); these results were then added to gives the EX of country j

at time t. The staple index was constructed in the same manner expect then using the staple

crop data.

c) The Model

A model was constructed to test the effect of agricultural prices and other variables on the

value added of an agricultural worker in a given country. Economic observations are often

dependent across time therefore in macroeconomics time series are often used to test a model.

It is assumed that the agricultural value added per worker (VA) is determined by other factors

across time when constructing the model for the determinants of the value added in African

46

states. For each variable the natural log was taken which corresponds with the change in

levels.

The variable VA denoted the value added per agricultural worker in a given country (j) at a

given time period (t). Agricultural value added per worker is often closely related to income.

(See section 4)

One would expect a higher revenue per hectare for the selected export crops, variable EX, to

result into a higher value added per agricultural worker. This because given the same costs,

inputs and weather conditions a higher price for the produce will result in a higher value

added per agricultural worker. However if the higher revenue is the result of, for example,

rising intermediate input costs then the revenue rise will not lead to a increase in value added.

This is important to note because the EX is a revenue index and does not take costs into

consideration. Following the same reasoning one can expect ST, which is the revenue per

hectare for the selected staple crops, to have a positive effect on the value added per

agricultural worker.

The variable GOV is the general government final consumption expenditure in constant 2000

US dollars. This variable contains all government current expenditure for purchases of goods

and services. The effect that GOV should have on the value added is hard to predetermine as

government expenditure in Africa is often anything but efficient and is often not directed

toward rural areas. One could expect that higher government expenditure would lead to higher

value added if this expenditure reflects investment in better roads or schools. However if a

rise in government expenditure only reflects higher overhead costs or military expenditure the

rise will most likely not lead benefits for the farmers. Over the course of recent African

history many governments did not operate in the public’s interest. Government expenditure is

therefore expected to have a limited impact or a negative impact on the agricultural value

added of African countries. Government expenditure is however added to the regression

because it could potentially have a huge impact on the value added of agricultural producers.

However given the African context the impact will most likely be negative and not

significant.

47

The variable ODA is the net official development assistance in constant 2000 US dollars. This

consists of disbursement of loans made on concessional terms and grants made by bilateral

and multilateral institutions to promote economic development and welfare in countries. It

also includes loans with a grant of at least 25 percent. ODA should hypothetically have a

positive effect on the development of a country. However given the limited impact of ODA on

the development in SSA countries one could expect that this variable to have no or a negative

impact on the value added. The impact, if any, is likely to be very small given the fact that a

decreasing percentage of ODA has gone to agriculture.

An extra variable was added to the regression for Rwanda. This was the variable GFCF which

is the gross fixed capital formation of a country in constant 2000 US dollars. This variable

consists of outlays on additions to the fixed assets of the economy. This variable for example

includes land improvements, plant, machinery and equipment purchases and the construction

of roads, railways and the like including schools, offices, private residential dwellings and

hospitals. Basically this variable captures the formation of capital in the broad sense of the

word. A high level of capital formation should have a positive effect on the development of a

country and possibly on the agricultural sector. GFCF reflects to a certain extent the

investments made in a country.

Another proxy for the level of agricultural investment was sought as GFCF was not available

for the other countries over the whole time period. Hayami and Ruttan use for their value

added analysis (Agricultural productivity differences among countries, 1970) farmland

(LAND) and livestock (LIVE) as proxies for investment in the agricultural sector in poor

countries. In Africa many farmers try to save in order to buy livestock. A case study

performed in Mali indicated that livestock was the savings account of choice for nearly all

farmers (Dixon, Tanyeri-Abus, and Wattenback 2004). Therefore livestock can be seen as a

proxy for the investment taking place in the agricultural sector by farmers. Livestock value

added is also included in the agricultural value added per worker which is another reason to

include it into the analysis. Hayami and Ruttan also argue that an addition to farmland

represent previous investment in that piece of land. The farmer, according to them, probably

had to invest in clearing the land, drainage, fencing and other development measures.

Therefore both variables could potentially be good proxies for the level of investment taking

place in the agricultural sector in third world countries. Both variables are therefore added to

48

the regression as a proxy for investment and are therefore expected to have a positive effect

on VA.

The variable FERT reflects the amount of fertilizer used by the farmers. The amount of

fertilizer should have a positive effect on the yield and therefore farm productivity. FERT is

therefore expected to have a positive effect on the VA.

The factors that influence the agricultural yields and productivity can be grouped into three

Categories (Gommes 1999). The first category is the continuous factors that change smoothly

over time but can change more abruptly for several years. Examples of the factors that fall

into this category are investments, irrigation, fertilizer input, human capital, and labour. The

second group of factors are the discontinuous factors or shocks, like government policy

changes, that can have huge implications for the agricultural sector. In Malawi market

liberalisation, which reduced the differential protection of large estates in the mid 1990’s,

enabled smallholders to produce cash crops. Many African governments made policy changes

in the late 1980’s and early 1990’s that had huge implications for the agricultural sector. But

also historical events like the genocide that took place in Rwanda in 1994 can have an

enormous impact. Discontinuous factors or shocks will influence the yield and value added of

the country frequently causing a break in the series. The third category of factors are the

pseudo-cyclic factors like weather patterns. The weather effects on the agricultural yield and

productivity of the weather irrigated smallholder farms in Africa.

The different categories of factors will have different influences on the value added of a

country. Continuous factor will influence the level of value added over time. They are often

good candidates for the explanatory variables in a value added regression. Discontinuous

factor or shocks will most likely influence the series by causing a jump in the series or by

changing the slope of the coefficients. The Rwandan genocide caused the value added per

worker to drop drastically in 1994. (See figure 20) It is therefore of importance to limit the

impact that these shocks have on the regression as they can disrupt the trend between the

explanatory variables and the dependent variable. This for example could be done by splitting

up the time series in such a way that 1994 is excluded from the regression. Or a constant

dummy could be added for the year that the genocide took place.

Figure 20: Rwanda

49

050

100150200250

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

 Agriculture value added per worker(constant 2000 US$)

Source: World Bank, WDI

The pseudo-cyclic factors will also influence the series. The weather to a certain extent will

explain the changes experienced in the yield. Higher fertilizer use when there is less rainfall in

a country will still lead to a lower value added than that it potentially could have been.

Therefore the weather will directly impact the value added in some cases causing the

continuous factors to have little or no impact on the value added. To a certain extent the value

added should be corrected for weather conditions as they influence the trend between the

value added and the other continuous factors. However in this paper not the value added but

the income of the farmer is of importance. Changes in the income of the farmer are more

clearly displayed when no corrections of weather are made. The consequence is that other

explanatory variables besides to the revenue indexes, which are multiplied by the yield, will

have a limited or no impact on the value added when putting it into a regression.

9) The regressions

a) The multiple regressions

The model that eventually was tested for the four SSA countries included all the variables that

empirically and theoretically should have a significant impact on the value added.(see section

7c for the model and section 4 for the empirical and theoretical discussion) When this

50

regression was performed for the selected countries the necessary problems were displayed.

Most multiple regressions will contain a certain degree of correlation between its explanatory

variable. However when the explanatory variables are highly correlated the multiple

regression will have problems with multicollinearity. So too did the regressions with this

model have the necessary problems with imperfect multicollinearity. When the R2 of a

regression is higher that 0.9 then there is evidence of problematic multicollinearity however

the regression also displayed other clear signs of imperfect multicollinearity. The main

problem with multicollinearity is that it diminishes the precision with which OLS estimators

are obtained. Therefore the regression might indicate that certain variables have insignificant

coefficients only because of the multicollinearity. Another consequence of multicollinearity is

that the signs of the estimated coefficients can be opposite of those expected. This is due to

the large standard errors, a consequence of multicollinearity, which increase the uncertainty

about the true parameter. Therefore the presence of problematic multicollinearity will have

big implications for the results of the regression. (See appendix 8 for table with results when

multicollinearity was present)

A simple correlation test between variables would seem to do the trick when detecting the

intercorrelated variables. This is the case with simple regressions however when regression

consist of more than two variables a simple correlation will not always indicate which

variables are the problem. Therefore auxiliary regressions have to be performed with the

variables displaying the symptoms as the dependent variable. When this is done for all

countries the variables LAND, LIVE, ST and FERT seem to be the problem. When an auxiliary

regression is performed with one of these variables as the explanatory variable then the other

three are usually highly significant. Also a correlation test was performed between all the

variables and these four variables were highly correlated with each other. To a certain extent

this is logical as there is often a direct relationship between these variables. The more

farmland a country has the more livestock it will probably have and more fertilizer it will use.

The link between ST and the other variables is most likely through to the yield per hectare

which is incorporated in to the index. This will cause the revenue index to be dependent on

fertilizer use.

Through certain adjustments an attempt was made to get rid of the multicollinearity between

the explanatory variables. The first adjustment made was that the variable LAND was omitted

from most of the analysis as it seemed to be the most problematic variable. The second

51

adjustment was that the variable FERT was divided by the amount of farmland in order to

obtain the amount of fertilizer used per hectare, FERT1. This new variable was not highly

correlated with LIVE. The last adjustment made was that the revenue indexes were replaced

by the export and staple price indexes PEX and PST. These indexes are similar to the revenue

indexes apart from the fact that they are not multiplied by the yield per hectare. (See equation

2.6) This causes these indexes to portray the real price per tonne for the selected export and

staple crops. These price indexes are not highly correlated with the variables LAND and

FERT1.

Another problem with the regression is that the variables ODA and GOV are also

intercorrelated to each other. This, to a certain extent, is logical as government expenditure in

SSA countries are often highly dependent on Official Development Assistance. Therefore

these two should not be put into the same regression together as this leads to problems with

multicollinearity.

It is also important to note that most of the regression performed had problems with serial

correlation. Usually when adding an AR(1) coefficient the regression passed the Breusch-

Godfrey serial correlation test. This is an iterative non-linear method for estimating

generalized differencing results with AR(1) errors in the presence of serial correlation

(Aterniou and Hall 2007). However the problem of serial correlation does indicate that the

regression might have important variables absent (Omitted variables), be misspecified or have

systematic errors in the measurement. The last factor might be the one that is most applicable

to the African regressions as there do seem to be errors in the data. Whether they are

systematic or not is hard to find out. However all the explanatory variables do have outliers in

the data series. These outliers might be due to measurement errors or poor estimations.

Therefore the problem of serial correlation seems to be inherent for African data as outliers in

the data seem to be more of a rule than an exception.

Table 4: LN regressions corrected for multicollinearity Cameroon Kenya Malawi Rwanda

PEX 0.042 -0.015 -0.241 0.020PST 0.027 0.038 0.193 -0.097GOV 0.0851 -0.043 -0.241*** -0.0528LAND -0.138 0.148 0.727** -0.053

Adjusted R2 0.964 0.563 0.561 0.525

52

BG 0.890 0.720 0.337 0.170White 0.46 0.010 0.007 0.032Ramsey 0.029 0.1902 0.239 0.2677***10 percent level ** 5 percent level *** 1 percent level BG = Breusch-Godfrey serial correlation LM testWhite=White Heteroskedasticity test Ramsey = Ramsey reset test

Table 5: LN regressions corrected for multicollinearityCameroon Kenya Malawi Rwanda

PEX 0.028 -0.013 0.058 -PST 0.035 0.031 0.058 -GOV 0.110 0.008 -0.404** -FERT1 0.024 -0.064 -0.068 -

Adjusted R2 0.964 0.575 0.585 -BG 0.835 0.580 0.615 -White 0.540 0.022 0.008 -Ramsey 0.162 0.346 0.305 -***10 percent level ** 5 percent level *** 1 percent level BG = Breusch-Godfrey serial correlation LM testWhite=White Heteroskedasticity test Ramsey = Ramsey reset test

The correction made solved the problems the model had with multicollinearity. However

when performing the regression almost none of the explanatory variables had a significant

impact on the value added on the SSA countries.(see table 4 and 5) Only Malawi displayed

significant results for the variable GOV which had a negative impact on the value added

However the regression had problems with heteroskedaticity and the errors were not normally

distributed. The lack of significant results could possibly be attributed to the presence

discontinuous factors that cause shocks in the series. Therefore slope dummies were added to

correct for policy changes and a constant dummy was added to the Rwandan series to take the

effect of the genocide into account. The policy changes made by the selected African

countries have been made over time. Therefore it was difficult to pinpoint at what point in

time the policy changes have effected of the agricultural sector. However generally speaking

the policy changes made in Africa have been made in the late 1980s and early 1990s. Due to

the fact that the exact time cannot be determined the slope dummy will be added that gives

values after 1990 a value of one and other years a value of zero. This because most policy

changes were made around this time so if the policy changes had any effect it would be in this

time frame.

53

The policy dummy did not have any significant impact on the regressions performed with

different combinations of explanatory factors for Cameroon, Kenya and Malawi. However the

constant dummy of Rwanda for the genocide was significant. Indicating that the genocide did

negatively impact the intercept of the regression in 1994. The regression below was one of the

regressions performed for Rwanda. The obtained coefficients are reported next to the variable

and its probability is reported in brackets below the coefficients.

lnVA = -1.955 -0.101lnST + 0.300lnEX -0.477GEN -0.006lnGOV + 0.395lnLIVE

(0.425) (0.337) (0.002) (0.000) (0.933) (0.024)

R2 = 0.638

This Regression passed all the necessary tests and showed no sign of misspecification, serial

correlation or heteroskedasticity. The results show that the dummy for the genocide, GEN, has

a significant negative impact on the slope of the value added of Rwanda as was expected. The

variables EX and LIVE are the two variables that have a significant impact on the value added

of Rwanda.17 Considering the fact that for all the countries the variables for farmland,

fertilizer use and livestock were highly correlated livestock can be seen as the variable

embodying the effect of all the three variables. Rwanda was the only country where the

variable EX had a positive significant impact on the value added. However it was also the

only country where no significant positive impact for the variable ST was obtained all the

regressions. Considering the fact that Rwanda is landlocked the reverse would be expected to

be true. However in all the four countries the revenue index was significantly higher than that

of the staple index in most time periods. Therefore it is probable that the value added of

export crops is a lot higher than that of staple crops. They might therefore, even though they

only constitute a small proportion of the total farmland, still have a significant impact on the

value added.

b) The single regressions

The main goal of the regression was to show the effect of price changes on the income of

farmer in SSA. Most multiple regressions show that prices have an insignificant effect on the

17 Rwanda had an incomplete series for the variable fertilizer. This variable could therefore not be included in the regression.

54

value added per agricultural worker. Given the structure of the market, perfect competition,

this result should not have been a surprise.

In order to retest the effect of the staple and export revenue on the value added a couple of

single regressions were performed between the two variables. This was done because

including other control variables caused problems with multicollinearity as the revenue index

often was influenced by the control variables. The weakness of these single regressions is that

important variables might be omitted from the regression. This might cause the export or

staple index to be significant while when other significant control variable would be added to

the regression this would not have been the case. However the presence of multicollinearity

gave no other option. Therefore the results that come out of these regressions should be

interpreted with caution. The results of the single regressions are presented in the table below.

Table 6: The results ST Cameroon Kenya (1966-

1990)

Malawi (1966-

1990)

Rwanda +

AR(2)

ST 0.076*** 0.134** 0.290* 0.015

R2 0.081 0.153 0.246 0.150

BG 0.893 0.673 0.911 0.210

White 0.350 0.582 0.864 0.762

Ramsey 0.262 0.456 0.354 0.382

Jarque-Bera 0.000 0.767 0.687 0.004

GEN - - - -0.333*

*** 10 percent level ** 5 percent level * 1 percent level

Table 7: The results EX Cameroon Kenya (1966-

1990)

Malawi (1966-

1990)

Rwanda +

AR(2)

EX 0.028 -0.027 -0.022 0.157*

R2 0.018 0.037 -0.042 0.232

BG 0.994 0.425 0.612 0.101

White 0.812 0.154 0.705 0.781

55

Ramsey 0.415 0.199 0.645 0.990

Jarque-Bera 0.000 0.230 0.528 0.058

GEN - - - 0.367**

*** 10 percent level ** 5 percent level * 1 percent level

The only variable that was significant for Cameroon was ST at a ten percent level. This

regression showed no sign of misspecification, heteroskedasticity or serial correlation. The

adjusted r-squared of this regression was, however, only 8.1 percent, which is very low. For

all the regressions performed for Cameroon the errors terms were not distributed normally.

This was probably due to the dependent variable which is not distributed normally but is

skewed to the right. Therefore the dependent variable was substituted by the total agricultural

value added which is distributed normally. When this was done the residuals became

normally distributed and the regression showed no signs of misspecification, heteroskedaticity

or serial correlation however also none of the variables were now significant. Figure 21

however does indicate the variables VA and ST do have a tendency to move together in some

years.

Figure 21: Cameroon: Percentage change in VA and ST

56

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

.5

1970 1975 1980 1985 1990 1995 2000 2005

VA ST

The FAO disclaimed that due to a lack of resources detailed work could not be performed for

the period ending in 1990. They also stated that for various known reasons the two data sets

may differ in the overlapping year 1991. The FAO statistic division would not give any

explanation as to why there is a difference between the two data sets. In this analysis the data

of the two periods are combined to form one series. When looking at the data this posed a

problem for some countries due to the fact that there was a price shock between the two series

for some commodities. This could be due to a normal shock as the price series jumps in

different years for different reasons. However a price shock experienced in 1991 is most

likely due to the difference between the two series of the FAO. Looking at figure 22 a price

shock in real producer price of tea for Rwanda can be observed in 1991. One might therefore

consider splitting up the regression in two samples when this problem is displayed; this might

lead to different results. In 1995 the series in the FAOSTAT was also interrupted for some

time due to resource constraints. This has led to the necessary problems with FAO data in the

second time period after 1995 as many data points were estimated because of this. Figures 21-

25 show that there are huge fluctuations in 1995 these fluctuations might be result of the

resource constraint. This is another reason to split up the series.

Figure 22: The tea price shock for Rwanda in 1991

57

Rwanda Tea

0

5001000

1500

2000

25003000

3500

4000

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Tea

Source: FAO

Following this reasoning the series for Kenya was split up into two periods; one from 1966-

1990 and the other from 1991-2005. Now the Kenyan regression did display significant

results for the variable ST in the first period however in the second period this variable was

not significant.(See appendix 9 for tables with results of second period regressions) Both

single regressions showed no signs of misspecification, serial correlation, and

heteroskedasticity and the errors were normally distributed. When looking at the two variables

in a graph it can be seen that the two variables on many occasions move in the same direction.

(See figure 23) Therefore there is a possibility that ST has a significant impact on the

dependent variable income. However in the period 1980 till 1990 the value added seems to

have remained constant. This might be due to the parastatals organisations that were

traditionally present in the Kenyan agricultural market and that of many other African

countries. These parastatal organisations controlled the bulk export commodities in Africa

they aimed to stabilize the prices received by the farmers. Many of these organisations were

abolished or restructured in the late 80’s and early 90’s. There are also possible outliers for ST

in the years 93, 94, and 95.

Figure 23: Kenya: Percentage change in VA and ST

58

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1970 1975 1980 1985 1990 1995 2000 2005

VA ST

For Malawi time series was also split up into two. As can be seen in figure 20 the variables ST

and VA do have the tendency to move in similar directions. The only period when this was

clearly not the case is in 1985 to 1990 when agricultural value added seems to have remained

stagnant. The result from splitting up the series resulted in ST being highly significant in the

first period with an adjusted r-squared of 25 percent. The regressions showed no signs of

serial correlation, hetroskedasticity and misspecification and the errors are distributed

normally. In the second series the variable ST was not significant even after correcting for

serial correlation.

Figure 24: Malawi: Percentage change in VA and ST

59

-.6

-.4

-.2

.0

.2

.4

.6

.8

1970 1975 1980 1985 1990 1995 2000 2005

VA ST

The results of Rwanda were different than of that obtained for other countries. The series of

Rwanda was also split up into two. In the first series the variable EX was highly significant

however the regression showed problems with heteroskedaticity. After correction for

heteroskedasticity using the White’s correction standard error estimates and performing the

regression again the variable EX remained significant at a 5 percent level. The regression

passed all the necessary tests and the errors were normally distributed. The reason why EX

does not have a significant impact on income in the second series (1991-2005) might be due

to the genocide. As the FAO estimated the producer prices in 1994 and 1995 which explains

part of the deviation. The series after 1993 display no clear trend between income and EX

something which before this timeframe was the case. From 1993 onward the two series seem

to move in opposite directions.

Figure 25: Rwanda: Percentage change in VA and EX

60

-.4

-.2

.0

.2

.4

.6

.8

1970 1975 1980 1985 1990 1995 2000 2005

VA EX

10) The results

There are two main results that come out of the regressions. The first one is that the export

revenue index does not seem to have significant impact of the value added of the farmer,

expect for Rwanda. The second main result is that the staple index does, when making the

necessary corrections, have a significant impact on the value added for three of the four

countries. However in all cases the adjusted r-squared is low. What does it indicate if the

revenue indexes have little or no impact on the agricultural value added per person?

It could, first of all, indicate that the dependent variable is a poor proxy to for the income of

the agricultural workers and farmers in third world countries. This is of course partly due to

the fact that the dependent variable comprises all the agricultural value added and not that of

crops alone. This limit the impact the export and staple revenue indexes will have on the

agricultural value added as they only tell one part of the story. Export crops production is also

relatively small compared to the staple crop production. It is therefore, to a certain extent, not

strange that the export crops index is not significant in three of the four countries given the

construction of the value added variable.

61

The lack of a significant impact of the independent variables might also be attributed to

measurement errors or outliers. Therefore the data was rechecked for inconspicuous looking

data points. The producer prices had the necessary problems with inconspicuous looking data

points. When looking at the data behind the regressions the heavy fluctuating revenue indexes

(ST and EX) seemed problematic. It is therefore important to find out why the indexes

fluctuate heavily in some years as inaccuracies in measurement may lead to outliers in the

data. The FAO states for example that the producer price does not always refer to the same

selling points as institutional set-up of the country sometimes differs over time. This

measurement inconsistency could in fact influence the series. In the forty years covered by the

producer price data a series of major event have happened in all four selected countries which

affect the producer price data. For Rwanda the genocide has a clear impact on the producer

price series. Many African countries have a turbulent history which impacts the series and

makes accurate measurement of the producer prices and other variables sometimes difficult.

Both staple and export revenue indexes had heavy price fluctuation in certain years. However

the jumps often happen in different years for different commodities and often show no real

trend. Although staple crops sometimes do have the tendency to jump and fall in similar

years. Therefore the impression is created that the real deflated prices are correct. The data

that lies behind the series, producer prices, have been rechecked. They however appear to be

correct in the sense that they correspond with what the FAO reports. Also in the FAO

producer price series there are heavy price fluctuations for many agricultural commodities.

Therefore the revenue indexes seem to portray the price changes correctly. The export

producer prices have also been cross checked with the world market prices of agricultural

commodities. There was for example a boom in coffee bean prices from 1992 till 2000 this

boom was also displayed in the producer price series of coffee for all the countries. That the

export producer prices are more volatile than the staple crop prices is nothing strange given

the nature of the market.

When assuming that the value added per agricultural worker is a good proxy for the income of

farmers and that the producer prices are correct then the results tell a story. Then the

regressions prove that revenue and price changes in the staple market and export market do

not have a huge impact on the income of the farmer/agricultural worker. Given the fact that in

most cases value added can be seen as a proxy for income one can conclude that revenue

62

changes have limited effect on the income of a farmer in third world countries. This result

corresponds with the structure of the staple and export market in third world countries and the

Cournot analysis.

The empirical results strengthen the evidence that price changes of the agricultural goods do

not affect the income of the agricultural workers over time. Large jumps in the price and

revenue indexes were often followed by small increases or no effect on in the value added per

agricultural worker. This indicates that the estimated price effects have a limited impact on

the producer incomes. Higher world prices will therefore not be a viable way to reduce

poverty and increase the income of farmers in SSA countries. The main reason for this is that

agricultural markets are often poorly integrated locally and international. This hinders a price

rise from automatically translating into an income rise for the farmers in the countries

researched.

This result does not have to conflict with the findings that price transmission has increased for

exporting firms (Dixon, Tanyeric-Abus, and Wattenback, 2004). The fact that export revenue

index had nearly no significant impact could be attributed to the relatively little farmland that

is used by the export sector. The value added might therefore be dominated by the agricultural

production for local consumption in Africa where the price transmission has been relatively

low (Dixon, Tanyeric-Abus, and Wattenback, 2004). This corresponds with the findings of

this paper as the staple revenue index had a very limited impact on the value added. However

in Kenya staple crops only account for 47 percent of the crop revenue while they occupy 78

percent of the farmland (Owour 1996). So even though export crops only account for a small

proportion of the farmland used they do, however, make up a large percentage to the total

revenue received for crops. Therefore one would have expected the export revenue indexes to

have a significant impact on the agricultural value added if prices were indeed transmitted to

the farmers. The empirical research of this paper however did not obtain a significant

coefficient for the export index in three of the four countries. Therefore the author of this

paper doubts that prices are completely transmitted to the export farmers.

The theory of exchange rate pass through can be used to understand why the price changes are

not completely translated into higher incomes. (See section 6c) The extent to which prices

pass through in the market depends on the market power. The producers of agricultural

commodities have no market power so for them the price pass through is complete. This

63

means that any price shock they experience is completely passed on to the buyers. The large

international firms that buy the product and sell it in other market do however often have

market power. They therefore have the ability to incompletely pass through the price. They

therefore lower their margin when the price of the agricultural commodity is high and

increase their margin when the price is low. This enables them to keep the profits for

themselves.

The empirical results of course apply to the agricultural sector overall. This does not imply

that small groups of producers might directly benefit from a price increase. However,

generally speaking, the impact of a price increase is limited. The empirical results also find

evidence that price changes in the staple market have bigger positive implications for the

incomes of a farmer overall than a price change experienced in the export sector. As the

empirical test find in three of the four countries a significant positive coefficient was obtained

for the variable ST when the necessary corrections were applied. However this significant

coefficient was usually only obtained in the series before 1991. It must also not be forgotten

that staple price increases have negative implications for most of the poor in these countries

(See section 3).

The potential of the staple market to increase the income of the farmers is something that

might seem straight forward when considering that most of the farmland is used to grow

staple crops. However the staple market operates in a more or less closed economy and is

solely dependent on local demand. The local demand is price inelastic which prohibits

farmers from producing a too high quantity as this will have negative implication for his or

her revenue. This discourages investors from investing in this sector. As stated before the

yield per hectare to a certain extent can be seen as an indicator for the investments that have

taken place within the sector (See section 7b). The yield per hectare of the staple crops has not

increased drastically for the selected SSA countries. This could be an indication that almost

no real investments have taken place within this sector over the past forty years. Maize, the

main staple crop of this region, has increased for only two of the four countries when

comparing the yield between 1966 and 2005. However for Malawi and Rwanda the yield per

hectare has slightly decreased when comparing the yield of 1966 to that of 2005. Overall yield

per hectare does not differ greatly from what they were around forty years ago. When

comparing the yield of the four African countries with that of Brazil a huge difference can be

observed. Until 1991 the yields per hectare of Brazil, Kenya and Cameroon were around the

64

same. However since then Brazil has massively increased its yield per hectare while that of

Cameroon and Kenya remained stable or even decreased.

Figure 26: Maize yield (tonnes per hectare)

0

0,5

1

1,5

2

2,5

3

3,5

4

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

CameroonKenyaMalawiRwandaBrazil

Source: FAO

Hayami and Ruttan argue that creating farmland represents a previous investment in that piece

of land. The farmer according to them probably had to invest in clearing the land, drainage,

fencing and other development measures. Following this reasoning an addition to the amount

farmland represents a form of agricultural investment. This would imply that the expansion of

farmland used to grow staples in the four SSA countries is a sign of investment in this

agricultural sector. However in Africa an addition to the farmland used to produce staple

commodities usually does not require a so called agricultural investment. In Africa, generally

speaking, the land is cleared by burning a plot of land and then crops are planted. The land is

often not fenced and no real other investments are made in this plot of land. Therefore it must

be argued that an increase in the amount of farmland does not involve a real investment in the

African agricultural sector as these authors imply.

The staple market, on which the livelihood of most of the farmers depends on, seems to have

remained stagnant in many African countries. This is of course due to many factors, some of

which have been discussed in this paper, which prohibit this sector from really flourishing.

(See sections 3 and 5a) However the empirics do indicate that staple commodity price

increases will benefit the farmers who grow these staple crops. As long as there is little

incentive for investments within this sector the staple sector will remain unchanged for

65

another forty years. Better market access for farmers can have huge implications for their

income. However this would imply setting many middlemen out of business. Of course a

more attractive staple market does not always have to be the result of direct investments

within the staple sector. Vietnam clearly demonstrates that a booming agricultural export

sector can benefit the staple growing farmers indirectly. One could also opt for better market

access of staple crops to world markets however this could work two ways. It could cause the

local farmers to benefit from increased demand due to the fact that they can now sell their

excess produce on world markets. This will remove the quantity restriction of domestic

demand that most staple crops have in SSA. However better market access could also back

fire as it would also make it easier for foreign exports to enter the domestic market. This

could cause the domestic production to fall as they cannot compete against foreign producers

disrupting the local market. Given the low yield in this sector and the high amount of

subsistence farmers that operate in it one can understand that the second scenario is one that is

not highly unlikely when there is better world market access. It therefore seems of importance

that first the domestic market access is improved before attempting to tap in on world

markets. This would increase the competitiveness of these staple crops before they tap in on

world markets. It could also cause farmers income to rise as farmers now have a more direct

link to local markets. Higher income could cause farmers to start investing in their own sector

which could slowly lead to efficiency gains. This strategy will prohibit food prices from

increasing drastically as efficiency gains are the main source for the rise in the incomes of the

farmer. This will limit the chances that the domestic food price will rise drastically and

negatively impact the poor within a country. Something which could be the case if foreign

markets are tapped to quickly.

Sometimes turmoil in other countries creates opportunities for the farmers without an

improvement market access having to take place. Malawi has for example recently benefited

from the turmoil in Zimbabwe as they now export maize to this once agricultural giant.

Zambia also exports food products to their troubled neighbour Congo. But these types of

benefits are usually only temporary. However these examples indicate that regional trade

might be of importance in the future.

Many African countries have been able in increase the yield per hectare of main export crops

sometimes to the same level as main international competitors. This, to a certain extent,

66

indicates that investment have taken place within this sector. For Kenya tea yield per hectare

has been around the levels of international competitors like Sri Lanka and India.

Figure 27: Tea yield (tonnes per hectare)

0

0,5

1

1,5

2

2,5

3

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

KenyaMalawiRwandaSri LankaIndia

Source: FAO

The coffee production however tells a different story. The international competition has been

fierce and the entrance of a new competitor Vietnam did not help. The yield of coffee beans

has fallen in Cameroon and Kenya this might be due to the increased focus on specialty

coffee. Rwanda has however seen its yield per hectare increase above that of Brazil but below

that of Vietnam. Tobacco shows a similar story there too the yield per hectare has improved

for both Malawi and Cameroon; however the yields are below that of main international

competitors like Brazil. Of course comparing SSA countries to an agricultural superpower

like Brazil is not completely fair. Many farms in Brazil have become heavily mechanised and

operate more like Western farms. However one must not forget that Brazil was not long ago a

third world country with potential. They used this potential to stimulate the agricultural sector

which is now thriving in Brazil. The African agricultural sector, even the export sector is still

dominated by small holders. They can therefore not be expected to obtain the same yield per

hectare as the heavily mechanized farms in Brazil. However the yields do indicate that

investments have taken place in this export sector, unlike the staple sector. Which, given the

revenue indexes, is logical as the potential benefits are a lot higher in the export markets than

in the staple market. However some of the export revenues, for example coffee, have

decreased over time.

67

Figure 28: Coffee yield (tonnes per hectare)

0

0,5

1

1,5

2

2,5

3

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

CameroonKenyaRwandaBrazilViet Nam

Source: FAO

Figure 29: Tobacco yield (tonnes per hectare)

0

0,5

1

1,5

2

2,5

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

BrazilMalawiCameroon

Source: FAO

11) Conclusions

68

The African agricultural market is complex in the sense that many different factors influence

this sector. The agricultural sector in Africa is, for example, divided into two main markets,

the export and the staple market, which have distinct characteristics. What the farmer

eventually produces is not completely determined by the farmer but also by other factors like

market access and government legislation. The agricultural sector in Africa is also often

subject to many constraints i.e. bad infrastructure and credit constraints. Due to these factors

and other factors the African agricultural market is very complex. The factors have also

caused price changes of agricultural commodities to have a limited impact of the incomes of

agricultural workers and producers, especially when referring to the staple market. This is

something that the empirical research of this paper confirms. Therefore the main question is

this: how can the markets operate in such a way that it causes farmers to earn higher incomes?

The author could think of potential options which might work. However the truth is that this

section will only be based on past research and the author’s own ideas. What eventually will

work in practice depends on many factors and might not at all correspond with the proposed

ideas. The basic fact remains that higher prices alone will have a limited affect on the income

of the farmers. Therefore if the poor farmers are to be truly helped in Africa then a higher

price alone will not be the solution. Therefore, given the structure of the market, one has to

wonder what strategy will increase the income of the farmers.

Productivity improvement, on which has been focused heavily by many researchers, will not

benefit the staple producer given the current structure of the market, with the likely exception

of Kenya. This mainly because the Kenyan staple market is more open than the other

countries researched in this thesis this mostly due to the coastal position and better

infrastructure.

Of course one could argue that the investments in infrastructure would improve market access

for the local farmers. Investment in infrastructure like roads in Africa has two main problems.

The first one is that maintenance barely happens and the second is that the farmers still need

middle men and intermediaries to get their goods to the markets as most farmers do not own

trucks or cars. The first problem is more striking than the second because massive investments

can take place in infrastructure projects however when the government has no money for

maintenance then the roads will quickly become useless. Given the limited amount of

69

finances that governments have in third world countries investing in roads to improve market

access of farmers may not be the best strategy. Unfortunately when private investment fails to

provide the necessary investment it is often left to the government to step in. Private

investment has barely taken place in the agricultural sector of African countries; it has

therefore often been left to the government to do so. However they have also neglected this

sector. The lack of investment in the rural areas might be due to the payoffs as they seem to

be minimal. If it was profitable to invest in this sector then private investment would have

stepped in. Money can only be spent once if an investor has to choose between a very

profitable investment in the city and a less profitable investment in the rural areas then the

decision is quickly made.

It is always easier to be critical of other propositions than propose something that might

actually help the agricultural sector. The author of this paper finds it hard to propose a

possible solution. However it seems that market access is a key in determining the income of

a farmer. Therefore better market access will most likely lead to higher incomes for farmers.

However the Cournot competition teaches us that, given the structure of the market, the price

will be equal to the marginal cost in the long run. However efficiency gains could lead to a

price that is higher than the marginal cost. Therefore if market access is improved efficiency

gains will be made which might enable farmers to start earning a higher income. The structure

of the market cannot be altered quickly; the obstacles however can be removed. One of these

obstacles is bad market access therefore improving market access is probably a key to

increasing the farmer’s income. With better market access price changes may start to

influence the income of the farmers. This can have positive but also negative impact on the

producer, as prices can rise but also fall. It is important to note that better market access to

foreign markets might backfire, especially given the fierce global competition. Given the low

yield and inefficiency of many African farms competing with foreign producers may not be

viable. Therefore first local improvements should be made before tapping in on world market.

The only problem with first improving the local agricultural sector is that currently there is no

incentive to do so as profits are low and difficult to make in this sector. Therefore it is easy to

say that the African agricultural sector should improve its market access but how this should

be done, given the current context, is a lot harder to propose. Government expenditure and

ODA alone are not going to be the solution, especially in the long run. It is not a matter of

money but a matter of incentive. Currently there is no incentive to invest and improve the

70

agricultural sector. The question is how can SSA create an attractive agricultural sector that

will benefit the farmers? The Kenyan horticultural sector is an example of a success story in

African agriculture. More of these innovative projects could slowly cause the agricultural

sector to improve as obstacles are tackled one by one. The farmers in Africa are not lazy and

do not want to be poor however. Unfortunately many of them are given no other option; they

therefore struggle for their survival. Give these hard working men and women an opportunity

to grasp the ladder out of poverty and many will do so. Providing this ladder should therefore

be a priority. The agricultural sector has the potential to hand this ladder to the farmers and

pull them out of poverty.

References:

71

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Bond, Marian E. (1983). “Agricultural responses to prices in Sub-Saharan African countries.” International Monetary Fund 30(4): 703-26

Bowen, Harry P., Abraham Hollander and Jean-Marie Viaene (1998). “Applied International Trade Analysis.” Londen: Palgrave Macmillan.

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Appendix:

Appendix 1: Cournot competition

As an example of an export market the coffee market will be used. There are an N amount of

producers that grow coffee beans. There are however only two firms that buy the coffee beans

firm Y and firm Z. The producers engage in Cournot competition, that is, they compete on the

amount the quantity they produce which in this case is simultaneously and independently

determined. Each farm interacts strategically with the other farms each time choosing the best

reaction to the quantity that the other farms will produce. The result of this strategic

interaction lead to numerous amounts of possible quantities the farm can produce, given the

reaction of the other farms, giving different prices. It is assumed that the farm is profit

maximising. This implies that he or she will choose a quantity, given the other farms best

reaction, which will maximize his or her profits. It assumed that farmers make this decision

simultaneously and independently. This means that all the farmers make their decision as to

what quantity to produce at the same time without knowing what the other farms will do.

The following description of competition can be put into mathematical formula. This analysis

starts with a simple aggregate demand function.

P = A – BQ (1.1)

The P is the price, A is the aggregate constant, B is the slope of the function and Q is the total

quantity the farms sell on the market. Q can also be written as follows:

(1.2)

In this formula q1 is the amount that farm 1 sells on the market, the more farms that produce a

certain commodity the longer this formula becomes. For simplicity the Cournot static model

will be solved for a various number of competitors from there on the author can demonstrate

how the solution would look like when there is perfect competition. The perfect competitive

result will look more like the result that will be obtained when looking at the small farms in

Africa. Of course the amount of farms matters for the outcome therefore the Cournot model

will first be solved for N amount farms, N indication an unknown amount. Given the fact that

there are N farms the profit function becomes:

(1.3)

The impact that farm 1’s quantity (q1) has on the price is – B, this term effects the intercept of

the demand function, shifting the demand function. Farm 2’s quantity has the exact same

75

effect on the demand function, mainly – B. This results in the effect that any change in the

quantity produced will result in a different demand curve thereby changing the profit

maximizing output of the farms. The profit maximizing output of a farm is determined by the

output where marginal costs equal marginal revenue. The marginal revenue (MR) can be

derived by taking the first derivative of the total revenue function with respect to output. What

is also known is the relationship between demand and marginal revenue. When the demand

curve is linear the marginal revenue function of a farm is similar to that of the demand curve

apart from the fact that it has twice the slope. The demand function is given by equation 1.3

therefore the marginal revenue function for firm qi is given by function 1.4. Q-i is the sum of

all the farms output except of that of farm i’s.

(1.4)

For this example it is assumed that there are constant marginal cost(MC) which are set at C.

Due to the fact that it is assumed that the N farms are identical they will produce the same

commodity at the same marginal costs. As stated before the profit maximising point is where

MC = MR. So for farm i this is where MRi = C:

(1.5)

Solving this for qi yields the best response function for farm i. The best response function is

in this case the profit maximizing reaction of farm i to any choice of industry Q-i’s output

decision. The best response function for farm i is:

(1.6)

This equation describes how firm i reacts to the output choice of Q-i, basically farm i’s output

(qi) is affected by the term – Q-i/2 . So an increase in quantity produced by industry Q-i will

lead to lower optimal production of output for farm i. Given the fact that MC for all farms are

the same(C) and the MR curves are symmetric the reaction curves of the N farms are also

symmetric to that of farm i. From this follows that, in equilibrium, each farm will produce the

same output. This means that so q* will denote the equilibrium output of

each firm. From this follows that Q-i = (N-1)q* plugging this into the equation gives the

following result.

(1.7)

The Nash equilibrium, this is the equilibrium in a noncooperative game where each player

has no incentive to change its strategy given the strategies of his rivals, is the point where the

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best response curves intersect. The result can be obtained by solving for q* . The exact

mathematical steps undertaken will not be displayed due to the limited scope of the paper.

However the following Nash equilibrium will be obtained for q*:

(1.8)

Due to the symmetry of this example all farms will have the same equilibrium output. This

means that N farms will produce Nq* of output. Therefore the total output of all farms (Q*) is

given by the following formula.

(1.9)

When substituting this into the demand function (equation 1.1) we obtain the equilibrium

price:

(2.0)

The profit (Π) for each firm is total revenue minus the total costs. This means that for farm i it

is P* qi* which are total revenues minus qi

*C which are its total costs. Due to the fact that the

farms are symmetric they all have the same profit function ( denotes equilibrium profit

per farm):

(2.1)

Appendix 2: The Merger paradox

As can be recalled from the Cournot analysis the profit(Π) of each firm is equal to:

(1.0)

unless the farmers are vertically integrated with the distribution of that crop there is no way to

prohibit other farmers from producing this crop selling it too. Now assume that M firms

merge where M is greater than or equal to 2. After the merger the amount of farms that

operate in the market are N-M+1 farms. Since it is assumed that all farm are identical the

merged firms can be thought of as comprised of firms 1 to M. From the previous analysis

equations 1.8, 1.9, 2.0 and 2.1 were constructed. These formulas are the outcomes of the

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Cournot analysis. The only difference between the merged equations in comparison to the

general equation is the N is replaced by N-M+1. Therefore qm* which is the output that the

merged firms will produce is given by the following formula:

(1.1)

The output of a non-merged firm which will be denoted by nm is given by the following

equation:

(1.2)

As can be seen from these two formulas qm* = qnm

* this means the merged firms become after

merging like any other farm within the industry. Therefore the price received for the

commodity will be the same for both non-merged firms and merged firms as will be their

profit. However given the fact that there are now fewer farms in the market(N > N-M+1) the

price of the commodity will be higher after the merger (Pam*)than before the merger(Pbm

*) as

indicated below. The non merged farms( ) benefit from the merger as their profit also

increases in comparison to before the merger( ).

The main question here is if it is profitable for the merged farms to merge? Prior to the merger

the M farms individually earned the profit of after the merger the M farms earn

collectively. In order for the merger to be profitable the following condition must

hold:

This condition states that in order for a merger to be profitable the profit of the merged farms

must be equal to or greater than the sum of the profits earned by the farms individually before

the merger. This then requires that:

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This condition is very difficult to satisfy even when more than two farms merge.

Therefore this condition clearly displays how difficult it is to merge profitably when Cournot

competition is taking place. Pepall, Richards and Norman in the book Industrial Organization

substitute M with aN (a being between 0 and one) into the equation above. This will imply

that a indicates the fractions of farms that merge. When this is done the following formula is

obtained:

The following equation indicates the fraction of farms that have to merge given the size of the

market in order for it be profitable. In Kenya it is estimated that there are around 400000

smallholders who grow tea. When plugging this figure into the formula as N it can seen that at

least 99,8 percent of the tea smallholders will have to merge in order for it to be profitable.

Showing that given the size of the agricultural industry forming a cooperative or a cartel

which is actually profitable is very difficult to achieve and would involve a process of

merging nearly all the smallholders.

Appendix 3: Higher food prices and the estimated effect on poverty worldwide

Table O.2 Higher food prices have increased both the incidence and severity of poverty worldwide January 2005–December 2007

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Initial levels: Change in:Region Poverty

headcount (%)Income gap ratio Poverty

headcount (% points)

Income gap ratio

Urban populationE.A and pacific 13,2 20,3 6,3 2,7Europe and central Asia

2,5 8,7 0,0 0,2

L.A and the Caribbean

3,7 37,6 0,1 -0,7

M.E and North Africa

2,7 17,8 2,4 5,7

S.A 34,1 38,1 1,7 0,3SSA 34,1 38,1 1,7 0,3Developing World

15,3 27,1 2,9 0,5

Rural populationE.A and pacific 31,9 23,2 4,9 0,7Europe and central Asia

8,2 6,6 0,0 0,0

L.A and the Caribbean

18,6 43,9 0,1 0,1

M.E and North Africa

15,4 22,9 0,7 0,9

S.A 43,3 24,0 0,8 0,3SSA 54,9 41,5 0,3 0,0Developing World

37,1 28,2 2,1 0,1

Source: World Bank, using the Global Income DistributionDynamics model.Note: The per capita poverty line equals 1.25 international2005 dollars a day. The ratio of food in total consumptionamong the poor is computed as described in De Hoyos andLessem (2008). East Asia excludes China, and the MiddleEast comprises Jordan, Morocco, and the Republic of Yemen.The income gap ratio expresses, as a percent of the povertyline, how much the income of the average poor person islower than the poverty line.

Appendix 4: The World Bank response on the problems with poverty data

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Feedback on the new international poverty line ($1.25 a day data)

This note offers some feedback to the World Bank on the country level international poverty line data provided through Povcalnet.

These comments came from DfID staff while verifying country level MDG progress. There are comments on specific data problems for several countries and also some feedback about using Povcalnet.

In addition to the points below some of our statistician have already raised concerns with you directly e.g. for Tanzania.

Ethiopia – concerns about the dramatic fall

We were surprised by the dramatic fall in poverty between 1999 and 2005 (17% pts). The 1995 and 1999 WB figures are much higher than for the national poverty line, but the 2005 WB figure is now exactly the same as for the national poverty line - creating a much more dramatic reduction in poverty than national figures suggest. Whilst there is a plausible story behind some degree of poverty reduction in Ethiopia, this degree of reduction is unexpected. We believe that the base data for all these estimate are the same – Ethiopia’s Household Income Consumption & Expenditure Surveys (HICES) for 1995, 1999 and 2005.

Could you explain the difference in the trends between the new international poverty line figures and the national poverty line figures?

Since the WB does not have micro data for 2005, we have used the decile distribution and the overall mean. The mean from 2005 survey shows a more than 20% increase, we have tried to double check this figure and was directed to country Statistical office’s report () which confirmed the increase.

National PL could be very different with the international PL so does the poverty measures. When there are interceptions on the poverty incidence curve, the trend could be different too (this is true for other countries so we will ignore the same questions here after). If DFIT can help to get the micro data for 2005, we will be more than happy to double check all results.

Zambia – 1998 data point out of line and 2006 survey data not included

We are broadly happy with the WB estimates but there are just two points we would like you to investigate.In most cases there is a reasonably good correspondence between WB estimates and the national surveys they are based on, but the 1998 data point seems out of line with this pattern.

What is the basis for the 1998 figure?The WB estimates do not reflect the LCMS 2006 survey, in the same series as previous surveys used for the WB estimates given here.

Can you add an estimate based on the 2006 Survey? 1. Zambia 1998 micro data file is from the WB African region and it is called “standardised data file”,

which indicates a good quality on the data processing.2. We will include 2006 data in our database as soon as we get the 2006 data.

Sierra Leone – seems too low and concerns about 1989 figure

The data for Sierra Leone is somewhat lower than the national poverty line and also seems low compared to other countries (e.g. lower than Nigeria and similar to Uganda).

Could you confirm that the data should be this low?Also, it is not clear where the 1989 data point came from.

What is the basis for the 1989 figure? National poverty line per month for Sierra Leone is 64223 at Sierra Leone dollar. Monthly poverty line for Sierra Leone based on $1.25/day is equal to 41446 Sierra Leone dollar. Hence $1.25/day poverty line is smaller than national poverty line which will give the smaller poverty rate than that of national poverty rate.

81

1989 data for Sierra Leone is from published grouped data. We would be very much happy to reconcile the data if there is chance to get the better data (i.e. micro data) for this year.

DRC – seems too low

The data point for DRC seems low compared to other countries (i.e. similar to Uganda). Could you confirm that the data should be this low?

We have got the micro data for DRC and will ask the region to double check the data.

Rwanda – 2006 survey data not included

The 2000 data point will be based on the national EICV1 survey. However, there was subsequently a second EICV2 survey carried out in 2006 which produced comparable poverty data. Both surveys produced robust data, were directly supported by DFID, and are central to Rwanda's poverty monitoring. Any assessment of poverty trends in Rwanda should take into account both the 2000 and 2006 data. It is not clear why the World Bank has not included the 2006 EICV2 survey in its new $1.25/day estimates: the results were released in April 2007 at an event attended by the World Bank.

Can you add an estimate based on the 2006 survey?

We do not have access of Rwanda 2006 data but we are looking for the data to include this data in our database if possible. Your help in this regard will be highly appreciated.

Uganda – data points are not comparable

The 1989 data point should be omitted from the WB trend data. In Uganda the baseline (and the first survey for which poverty data are calculated) is the 1992/93 Integrated Household Survey rather than the 1989 Household Budget Survey for the following reasons: a) the 1989 survey only covered part of the country and b) the design of the Household Budget Survey (in particular the questionnaire design) was different to that of later surveys. Also, Data for 93-99 are not strictly comparable with 1992, 2002 and 2005 as they do not cover the whole country.

Can you remove 1989 and other incomparable data points?

Could you please send us the documents about 1989 survey and the comparison problems for other years? so that we can document these problems in PovcalNet.

Somalia – no data

In the previous $1 a day data there were several data points for Somalia but the new estimates have no data. Why was the data for Somalia not updated?

Until now we do not have data access Somalia but we are more than happy to include them in our database. Your help in this regard would be highly appreciated.

Malawi – seems too high and different trend compared to national poverty line

I am surprised by the high proportions (83% in 1997 and 74% in 2004) estimated to be living below the international poverty line. It is much higher than most of the other African countries. The trend is also inconsistent with the national poverty line data which indicates that, over roughly the same period, the headcount fell only very slightly from 54% in 1998 to 52% in 2004. I understand that Malawi have participated in all previous rounds of the ICP so I am also surprised by the huge jump from the previously published data (21%, 2004 - not viewed as credible by the country office) to these latest figures although I appreciate the more robust PPP information etc.

Could you explain these high proportions and different trends?

The national poverty line for Malawi 2004 is 1347/mo whereas the monthly poverty line based on $1.25/day is 1961 at local currency. Since these poverty lines are different, we can expect the different trend due to the level of poverty line. (please see the answers above)

Mozambique – seems too high and different trend compared to national poverty line

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The proportions for Mozambique also seem high (81%, 1996 to 75%, 2002). The national poverty line also indicates a decrease although this is greater than what we're seeing in the WB figures (69%, 1997 to 54%, 2003). Mozambique didn't participate in earlier ICP rounds which could explain the big difference to previous WB estimates. Again, I think more work is required to understand the absolute level and less steep declining trend.

Could you explain these high proportions and different trends?

It needs to explain the same logic that has been mentioned in Malawi case.

Cambodia – data points not comparable and 2007 survey not included

The 1994 data point (49%) only covers parts of the country that were accessible (i.e. excludes areas under Khmer Rouge control) - this underestimates the level of poverty. The 2004 figure of 40% covers the whole country - so drawing a straight line between these two points will underestimate the trend in poverty reduction. Using the national poverty line and poverty trends within the directly comparable geographical sampling frame of the 1994 survey, the figures for the whole of Cambodia become 47% (1994) to 35% (2004) would gives a much more optimistic trend (1.23 percentage points p.a.). Over the last three years the national poverty line appears to have declined from 35% (2004) to 30% in 2007 (or 1.53 percentage points p.a.). If this rate of the last three years could be sustained, Cambodia would definitely exceed its target (poverty would have fallen from over 47% in 1994 to under 18% in 2015. (Source: as explained by WB Poverty Specialist)

Can the data be produced on a comparable basis and the 2007 survey included?

The WB country team has passed 2007 data to us but we found the sampling weight is wrong. It will be included as soon as we get the right data.

We know that 1994 and 2004 surveys are not comparable. 1994 survey did not cover the whole country due to security reason.

Tajikistan – basis for 2004 data?

It is not clear where the data point for 2004 comes from – there are no source details on Povcalnet. Also there is a very sharp and unexplainable drop from 2003 to 2004.

What is the basis for the 2004 data?

The micro data is from East Asia Region of the WB. The name of the survey for 2004 is: Household survey (income/expenditure household survey). For 2003, it is Living Standards Measurement Survey.

Kyrgyzstan – concerns about 1999 data and missing data points

1999 was the year of the financial crisis in Russia / CIS and should not be the year for a sharp drop in poverty. There should also be data for 2000-2003 from the Household Budget Survey and from 2003 onwards from the Kyrgyz Integrated Household Survey (see Annex1 of the link below):http://siteresources.worldbank.org/ECAEXT/Resources/publications/454763-1191958320976/Poverty_assessment_Vol1.pdf

The 1999 estimates are out of trend. We are still waiting for the original source of the data. We have got 2000 and 2003 data and we will include them in the PovcalNet soon.

Moldova – missing data pointsThere should be data for all years from 1999-2006 from the Household Budget Survey.

As you suggested, there are many surveys that are not covered in the existing version of PovcalNet. We will update all available surveys in the PovcalNet soon. We are always looking for new surveys to include in our database. We would like to request to help in this regard.

Pakistan – large variation

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The WB series is very ‘jumpy’ and questionable. It is expected poverty increased through the 90’s. It is highly unlikely however that it went from 65 in 1990 down to 23 in 2 years back up to 48 in 1996 and down massively again to 29 in 1998, as the WB data suggests. The WB figure of 65% in 1990/91 does not seem credible. Pakistan’s MDG report uses 26.1% in 1990/91. This figure is in line with FBS data of 26.6 for 1992/93 (national poverty line). WB data for years since 1998 are (more or less) in line with data published in Pakistan, differing by at most 1.5pp and 1 year, and showing a similar trend. It is not clear what explains the variation in the years (something to raise with WB), but the differences in headcounts may be due to the differences in definition ($1 day vs national poverty line). However, given the doubtfulness on the first part of the WB series, I would suggest not using that data at all until they can review the whole series and clean out anomalies. Instead, the local data can be used (though they do refer to the national poverty line). It is expected there has been a real decrease in poverty between 2001 and 2006, even if the size of the reduction is debated. GoP data suggest a 12pp drop. WB analysis based on the same raw data but different estimation strategies suggest a drop between 5 and 10 pp.

Can you explain the variation between the data points?

There are three different surveys in Pakistan over this period. Please see attached table for detailed information: Labour Force Survey 2004/2005 1305.35 2004/2005 micro

Labour Force Survey 2002 938.67 2002 microIntegrated Household Survey (Round 2)

1996/1997 619.28 1996/1997 micro

Integrated Household Survey (Round 1)

1992/1993 407.15 1992/1993 micro

Pakistan Integrated Household Survey (PIHS)

1991 271.05 1991 micro

Household Income and Expenditure Survey

1987 197.02 1987 micro

It is clear that these surveys are not comparable. This is a long standing problem and will appreciate any help from DFIT and other international organizations.

Nepal – seems too high

The trend seems correct but the value seems much too high compared to other Asian countries. It seems to have been revised up more than other Asian countries e.g. it now has the highest rate of poverty in Asia but based on the old estimates there were a number of countries with higher rates.

Can you confirm that the data for Nepal should be so high?

It is because of the PPP rates. PPP 1993 rates for Nepal was 9.236 whereas is 26.467 for PPP 2005. The Price level index rise from 19 at 1993 to 37 at 2005 (US=100). The change for Nepal is bigger than Bangladesh, India and Pakistan.

South Africa

The only real issue is that WB appear to have missed the 2005/06 income and expenditure survey for SA which is unfortunate as it means the most recent info presented is for 2000.

Is it possible to add another data point covering the most recent survey?

The 2005/06 survey data the WB got seems not right (the income Gini is 80%+). We will try to include 2005/06 poverty estimates for SA as soon as we get the right data.

Tanzania

- WB data shows poverty increasing, whereas local poverty line data shows a small decrease over the same period.- Tanzania appears to be the poorest country in SSA according to this data, and we cant believe this is true.

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We are using the official CPI from Statistical Office of Tanzania (recent updates of CPI) whereas the "local poverty line" estimates uses the survey price from 1991/92 to 2000/01.

Povcalnet

The download format was not user friendly. I was just interested in one data series (% below the international poverty line) but there was not an option to download a time series of one data set for all countries. Instead it came out as a separate row for each survey year for each country. So I had to spend a bit of time putting the data into a format that was useful. It would be helpful if there was an option to look at each series separately with the survey years in columns instead of rows. It is based on your choice and different bottom you used. But you can always copy and paste the data to EXCEL and sort the data—please notice that we only have very small budget to maintain/update the PovcalNet.

The series names were not very clear – it was not particularly clear that (H%) referred to the proportion below the international poverty line. There is a pop up window explaining each of the name, i.e.:

• Headcount (H):

% of population living in households with consumption or income per person below the poverty line.

It was not clear on Povcalnet that it is just using Poverty PPPs as opposed to standard PPPs. Same as the above.

The survey years were also quite confusing. I understand that the data might come from a survey conducted over two years and so you have presented it as an average but this caused problems as I presented the data in a time series and had to choose a single year to put the data in. Same as the above.

It would also be useful to have the actual numbers of the population living below the poverty line. I noticed that the population figures are there for most countries when you click on the second button – so I’m assuming this might be easy enough to do.

Also, the number of delays to the publication of the data on Povcalnet was disappointing – we didn’t find out about these delays until the day data was supposed to be published. It would have been helpful to be notified of these delays in advance.

Appendix 5: The log of the tea producer price(LNTEAEX) and the log of the tea export price(LNTEAPRO) of Rwanda

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5.5

6.0

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1970 1975 1980 1985 1990 1995 2000 2005

LNTEAEX LNTEAPRO

Appendix 6: Specifics on the indexes

Kenya deflated series 1966 – 2005 Min (year) Max (year) Median Average

StapleCassava 58,77 (90) 129,51 (94) 72,57305 79,36951

Maize 78,48 (70) 258,47 (94) 139,9581 145,1613Sorghum 74,77 (70) 368,21 (92) 124,3883 154,413

Wheat 128,70 (70) 326,49 (94) 211,9681 214,2692Export

Coffee, green 1165,15 (03) 6210,23 (77) 2469,636 2794,007Maize 78,48 (70) 258,47 (94) 139,9581 145,1613

Pineapples 36,00 (82) 196,71 (94) 60,37323 84,48241Tea 1168,74 (92) 4453,51 (84) 1922,207 2026,116

Cameroon deflated series 1966-2005

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Min (year) Max (year) Median AverageStaple

Cassava 36,31 (94) 137,94 (92) 89,01 88,57Maize 82,11 (04) 313,82 (79) 182,25 188,00

Plantains 41,38 (01) 176,45 (05) 119,76 114,08Rice, paddy 86,12 (94) 612,78 (78) 192,56 238,66

Sorghum 90,66 (94) 344,44 (99) 183,80 178,99Export

Bananas 59,49 (94) 185,45 (98) 91,28 95,92Cocoa beans 339,97 (94) 1463,02 (05) 881,64 878,32

Coffee, green 247,80 (05) 2135,23 (66) 1167,74 1032,99Cotton lint 394,56 (93) 1099,02 (66) 748,32 717,46

Cottonseed 51,68 (93) 109,90 (66) 88,05 86,62Natural rubber 231,34 (00) 1318,82 (66) 682,07 666,23

Tobacco, unmanufactured 481,22 (84) 2442,97 (01) 1089,32 1243,67

Malawi deflated series 1966-2005

Min (year) Max (year) Median AverageStaple

Cassava 21,02 (83) 74,04 (70) 35,87 37,32Maize 45,38 (70) 119,85 (01) 63,71 68,51

Potatoes 124,01 (66) 361,73 (86) 201,15 203,77Export

Sugar cane 14,36 (81) 42,10 (01) 21,66 22,84Tea 336,97 (05) 1319,40 (68) 612,70 638,48

Tobacco, unmanufactured 301,18 (05) 743,91 (01) 487,12 474,87

Rwanda deflated series 1966 – 2005

Min (year) Max (year) Median AverageStaple

Beans, dry 193,09 (05) 485,90 (69) 286,62 320,64Cassava 49,47 (77) 262,94 (98) 115,43 124,27

Maize 103,53 (05) 269,17 (78) 159,58 167,40Plantains 55,59 (77) 257,03 (94) 93,63 99,32

Sorghum 137,86 (05) 371,49 (95) 208,83 221,93Sweet potatoes 31,28 (05) 140,17 (78) 83,48 86,83

ExportCoffee, green 199,75 (05) 2415,56 (66) 1147,11 1189,75

Tea 580,12 (90) 3589,63 (95) 1368,29 1461,63

Appendix 7: Graphs of the indexes

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Staple revenue index

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Appendix 8: Regressions with multicollinearity problems

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Table: LN regression results Cameroon Kenya Malawi Rwanda

EX -0.005 -0.015 0.151 0.244**ST 0.080 0.062 -0.051 -0.073GOV 0.116 -0.026 -0.639*** 0.067ODA 0.029 -0.020 0.0152** -0.061LAND -0.015 0.084 -0.292 0.236LIVE -0.061 0.037 0.730** 0.136FERT 0.022 -0.041 0.095 -

Adjusted R2 0.963 0.557 0.606 0.510BG 0.768 0.758 0.127 0.107White 0.740 0.006 0.327 0.297Ramsey 0.002 0.027 0.707 0.000***10 percent level ** 5 percent level *** 1 percent level BG = Breusch-Godfrey serial correlation LM testWhite=White Heteroskedasticity test Ramsey = Ramsey reset test

Table: Percentage regression results Cameroon Kenya Malawi Rwanda

EX -0.0049 -0.0196 -0.194 0.028*ST 0.078 0.046 0.312*** -0.153GOV 0.093 0.052 -0.342 -0.187*ODA 0.093 0.052 -0.342 -0.137LAND -0.137 0.087 0.781 0.501*LIVE -0.057 0.030 -0.149 -0.318FERT -0.005 -0.028 0.025 -

Adjusted R2 -0.054 -0.021 0.480 0.184BG 0.916 0.736 0.111 0.394White 0.893 0.648 0.511 0.747Ramsey 0.059 0.149 0.001 0.430***10 percent level ** 5 percent level *** 1 percent level BG = Breusch-Godfrey serial correlation LM testWhite=White Heteroskedasticity test Ramsey = Ramsey reset test

Appendix 9: Single regression results

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Table: The results ST

Cameroon (1966-1990)

Cameroon(1991-2005)

Kenya(1991-2005)

Malawi(1991-2005)

Rwanda (1966-1990)

Rwanda(1991-2005)

ST 0.049 0.102* -0.007 0.279 0.114 -0.152

R2 -0.028 0.447 -0.074 0.371 -0.026 0.360

BG 0.940 0.514 0.620 0.544 0.136 0.155

White 0.061 0.394 0.926 0.121 0.455 0.273

Ramsey 0.065 0.572 0.117 0.152 0.772 0.402

Jarque-

Bera

0.171 0.001 0.726 0.601 0.027 0.503

***10 percent level ** 5 percent level * 1 percent level

Table: The results EX

Cameroon (1966-1990)

Cameroon(1991-2005)

Kenya(1991-2005)

Malawi(1991-2005)

Rwanda (1966-1990)

Rwanda(1991-2005)

EX 0.000 0.036 -0.0132 -0.291 0.303** -0.036

R2 -0.045 0.064 -0.151 0.077 0.196 0.514

BG 0.920 0.539 0.668 0.145 0.295 0.822

White 0.682 0.197 0.864 0.153 0.023 0.913

Ramsey 0.807 0.805 0.111 0.614 0.189 0.005

Jarque-

Bera

0.019 0.070 0.708 0.881 0.868 0.868

GEN - - - - -0.332*** -

***10 percent level ** 5 percent level * 1 percent level

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